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Meder B, Asselbergs FW, Ashley E. Artificial intelligence to improve cardiovascular population health. Eur Heart J 2025; 46:1907-1916. [PMID: 40106837 PMCID: PMC12093147 DOI: 10.1093/eurheartj/ehaf125] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2024] [Revised: 12/23/2024] [Accepted: 02/17/2025] [Indexed: 03/22/2025] Open
Abstract
With the advent of artificial intelligence (AI), novel opportunities arise to revolutionize healthcare delivery and improve population health. This review provides a state-of-the-art overview of recent advancements in AI technologies and their applications in enhancing cardiovascular health at the population level. From predictive analytics to personalized interventions, AI-driven approaches are increasingly being utilized to analyse vast amounts of healthcare data, uncover disease patterns, and optimize resource allocation. Furthermore, AI-enabled technologies such as wearable devices and remote monitoring systems facilitate continuous cardiac monitoring, early detection of diseases, and promise more timely interventions. Additionally, AI-powered systems aid healthcare professionals in clinical decision-making processes, thereby improving accuracy and treatment effectiveness. By using AI systems to augment existing data sources, such as registries and biobanks, completely new research questions can be addressed to identify novel mechanisms and pharmaceutical targets. Despite this remarkable potential of AI in enhancing population health, challenges related to legal issues, data privacy, algorithm bias, and ethical considerations must be addressed to ensure equitable access and improved outcomes for all individuals.
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Affiliation(s)
- Benjamin Meder
- Precision Digital Health and Informatics for Life, Clinic of Cardiology, Angiology and Pulmonology, University of Heidelberg, Im Neuenheimer Feld 410, Heidelberg 69120, Germany
- German Center for Cardiovascular Research (DZHK) Partnerside Heidelberg, Heidelberg, Germany
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centre, University of Amsterdam, Amsterdam, The Netherlands
- Institute of Health Informatics, University College London, London, UK
- National Institute for Health Research, University College London Hospitals, Biomedical Research Centre, University College London, London, UK
| | - Euan Ashley
- Departments of Medicine, Genetics, and Biomedical Data Science Stanford University, 870 Quarry Road, Stanford, CA, USA
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Szugye NA, Mahalingam N, Somasundaram E, Villa C, Segala J, Segala M, Zafar F, Morales DLS, Moore RA. Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching. Pediatr Cardiol 2025; 46:590-598. [PMID: 38570368 PMCID: PMC11842492 DOI: 10.1007/s00246-024-03470-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/07/2024] [Indexed: 04/05/2024]
Abstract
Total Cardiac Volume (TCV)-based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a DenseNet architecture in combination with residual blocks of ResNet architecture. The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). A deep learning-based 3D-CNN model can provide accurate automatic measurement of TCV from CT images. This initial study is limited as a single-center study, though future multicenter studies may enable generalizable and more accurate TCV measurement by inclusion of more diverse cardiac pathology and increasing the training data.
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Affiliation(s)
- Nicholas A Szugye
- Cleveland Clinic Foundation, Pediatric Cardiology, Cleveland, OH, USA.
| | - Neeraja Mahalingam
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | | | - Chet Villa
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | | | | | - Farhan Zafar
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - David L S Morales
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
| | - Ryan A Moore
- Cincinnati Children's Hospital Medical Center, 3333 Burnet Avenue, Cincinnati, OH, 45229, USA
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Schmitt R, Schlett CL, Sperl JI, Rapaka S, Jacob AJ, Hein M, Hagar MT, Ruile P, Westermann D, Soschynski M, Bamberg F, Schuppert C. Fully Automated Assessment of Cardiac Chamber Volumes and Myocardial Mass on Non-Contrast Chest CT with a Deep Learning Model: Validation Against Cardiac MR. Diagnostics (Basel) 2024; 14:2884. [PMID: 39767245 PMCID: PMC11675647 DOI: 10.3390/diagnostics14242884] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2024] [Revised: 12/16/2024] [Accepted: 12/20/2024] [Indexed: 01/11/2025] Open
Abstract
Background: To validate the automated quantification of cardiac chamber volumes and myocardial mass on non-contrast chest CT using cardiac MR (CMR) as a reference. Methods: We retrospectively included 53 consecutive patients who received non-contrast chest CT and CMR within three weeks. A deep learning model created cardiac segmentations on axial soft-tissue reconstructions from CT, covering all four cardiac chambers and the left ventricular myocardium. Segmentations on CMR cine short-axis and long-axis images served as a reference. Standard estimates of diagnostic accuracy were calculated for ventricular volumes at end-diastole and end-systole (LVEDV, LVESV, RVEDV, RVESV), left ventricular mass (LVM), and atrial volumes (LA, RA) at ventricular end-diastole. A qualitative assessment noted segmentation issues. Results: The deep learning model generated CT measurements for 52 of the 53 patients (98%). Based on CMR measurements, the average LVEDV was 166 ± 64 mL, RVEDV was 144 ± 51 mL, and LVM was 115 ± 39 g. The CT measurements correlated well with CMR measurements for LVEDV, LVESV, and LVM (ICC = 0.85, ICC = 0.84, and ICC = 0.91; all p < 0.001) and RVEDV and RVESV (ICC = 0.79 and ICC= 0.78; both p < 0.001), and moderately well with LA and RA (ICC = 0.74 and ICC = 0.61; both p < 0.001). Absolute agreements likewise favored LVEDV, LVM, and RVEDV. ECG-gating did not relevantly influence the results. The CT results correctly identified 7/15 LV and 1/1 RV as dilated (one and six false positives, respectively). Major qualitative issues were found in three cases (6%). Conclusions: Automated cardiac chamber volume and myocardial mass quantification on non-contrast chest CT produced viable measurements in this retrospective sample. Relevance Statement: An automated cardiac assessment on non-contrast chest CT provides quantitative morphological data on the heart, enabling a preliminary organ evaluation that aids in incidentally identifying at-risk patients who may benefit from a more targeted diagnostic workup.
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Affiliation(s)
- Ramona Schmitt
- Department of Cardiology and Angiology, University Heart Center Freiburg—Bad Krozingen, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Südring 15, 79189 Bad Krozingen, Germany
| | - Christopher L. Schlett
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
| | | | - Saikiran Rapaka
- Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA
| | - Athira J. Jacob
- Siemens Healthineers, 755 College Rd E, Princeton, NJ 08540, USA
| | - Manuel Hein
- Department of Cardiology and Angiology, University Heart Center Freiburg—Bad Krozingen, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Südring 15, 79189 Bad Krozingen, Germany
| | - Muhammad Taha Hagar
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
| | - Philipp Ruile
- Department of Cardiology and Angiology, University Heart Center Freiburg—Bad Krozingen, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Südring 15, 79189 Bad Krozingen, Germany
| | - Dirk Westermann
- Department of Cardiology and Angiology, University Heart Center Freiburg—Bad Krozingen, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Südring 15, 79189 Bad Krozingen, Germany
| | - Martin Soschynski
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
| | - Fabian Bamberg
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
| | - Christopher Schuppert
- Department of Diagnostic and Interventional Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Hugstetter Str. 55, 79106 Freiburg im Breisgau, Germany
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Barcellini A, Rordorf R, Dusi V, Fontana G, Pepe A, Vai A, Schirinzi S, Vitolo V, Orlandi E, Greco A. Pilot study to assess the early cardiac safety of carbon ion radiotherapy for intra- and para-cardiac tumours. Strahlenther Onkol 2024; 200:1080-1087. [PMID: 39212688 DOI: 10.1007/s00066-024-02270-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Accepted: 07/03/2024] [Indexed: 09/04/2024]
Abstract
PURPOSE Modern photon radiotherapy effectively spares cardiac structures more than previous volumetric approaches. Still, it is related to non-negligible cardiac toxicity due to the low-dose bath of surrounding normal tissues. However, the dosimetric advantages of particle radiotherapy make it a promising treatment for para- and intra-cardiac tumours. In the current short report, we evaluate the cardiac safety profile of carbon ion radiotherapy (CIRT) for radioresistant intra- and para-cardiac malignancies in a real-world setting. METHODS We retrospectively analysed serum biomarkers (TnI, CRP and NT-proBNP), echocardiographic, and both 12-lead and 24-hour Holter electrocardiogram (ECG) data of consecutive patients with radioresistant intra- and para-cardiac tumours irradiated with CIRT between June 2019 and September 2022. In the CIRT planning optimization process, to minimize the delivered doses, we contoured and gave a high priority to the cardiac substructures. Weekly re-evaluative 4D computed tomography scans were carried out throughout the treatment. RESULTS A total of 16 patients with intra- and para-cardiac localizations of radioresistant tumours were treated up to a total dose of 70.4 Gy relative biological effectiveness (RBE) and a mean heart dose of 2.41 Gy(RBE). We did not record any significant variation of the analysed serum biomarkers after CIRT nor significant changes of echocardiographic features, biventricular strain, or 12-lead and 24-hour Holter ECG parameters during 6 months of follow-up. CONCLUSION Our pilot study suggests that carbon ion radiotherapy is a promising radiation technique capable of sparing off-target side effects at the cardiac level. A larger cohort, long-term follow-up and further prospective studies are needed to confirm these findings.
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Affiliation(s)
- Amelia Barcellini
- Department of Internal Medicine and Therapeutics, University of Pavia, 27100, Pavia, Italy
- Radiation Oncology Unit, Clinical Department, CNAO National Center for Oncological Hadrontherapy, 27100, Pavia, Italy
| | - Roberto Rordorf
- Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy
- Arrhythmia and Electrophysiology Unit, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy
| | - Veronica Dusi
- Division of Cardiology, Department of Medical Sciences, University of Turin, 10126, Torino, Italy
| | - Giulia Fontana
- Clinical Department, CNAO National Center for Oncological Hadrontherapy, Via Erminio Borloni 1, 27100, Pavia, Italy.
| | - Antonella Pepe
- Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy
- Division of Cardiology, Cardio-Thoracic Department, San Carlo Borromeo Hospital (ASST Santi Paolo e Carlo), 20100, Milano, Italy
| | - Alessandro Vai
- Medical Physics Unit, Clinical Department, CNAO National Center for Oncological Hadrontherapy, 27100, Pavia, Italy
| | - Sandra Schirinzi
- Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy
| | - Viviana Vitolo
- Radiation Oncology Unit, Clinical Department, CNAO National Center for Oncological Hadrontherapy, 27100, Pavia, Italy
| | - Ester Orlandi
- Radiation Oncology Unit, Clinical Department, CNAO National Center for Oncological Hadrontherapy, 27100, Pavia, Italy
- Department of Clinical, Surgical, Diagnostic, and Pediatric Sciences, University of Pavia, 27100, Pavia, Italy
| | - Alessandra Greco
- Division of Cardiology, Fondazione IRCCS Policlinico San Matteo, 27100, Pavia, Italy
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Han D, Shanbhag A, Miller RJH, Kwok N, Waechter P, Builoff V, Newby DE, Dey D, Berman DS, Slomka P. AI-Derived Left Ventricular Mass From Noncontrast Cardiac CT: Correlation With Contrast CT Angiography and CMR. JACC. ADVANCES 2024; 3:101249. [PMID: 39309658 PMCID: PMC11416662 DOI: 10.1016/j.jacadv.2024.101249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 08/04/2024] [Accepted: 08/13/2024] [Indexed: 09/25/2024]
Abstract
Background Noncontrast computed tomography (CT) scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (CMR). Objectives The purpose of the study was to assess the feasibility of LV mass estimation from standard, ECG-gated, noncontrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and CMR. Methods We enrolled consecutive patients who underwent coronary CTA, which included noncontrast CT calcium scanning and contrast CTA, and CMR. The median interval between coronary CTA and CMR was 22 days (interquartile range: 3-76). We utilized a no new UNet AI model that automatically segmented noncontrast CT structures. AI measurement of LV mass was compared to contrast CTA and CMR. Results A total of 316 patients (age: 57.1 ± 16.7 years, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r = 0.84, P < 0.001), with no significant differences (136.5 ± 55.3 g vs 139.6 ± 56.9 g, P = 0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to CMR, measured LV mass was higher with AI (136.5 ± 55.3 g vs 127.1 ± 53.1 g, P < 0.001). There was an excellent correlation between AI and CMR (r = 0.85, P < 0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and CMR or AI and CMR. Conclusions The AI-based automated estimation of LV mass from noncontrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and CMR.
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Affiliation(s)
- Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California, USA
| | - Robert JH. Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
- Department of Cardiac Sciences, University of Calgary, Calgary, Alberta, Canada
| | - Nicholas Kwok
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Parker Waechter
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - David E. Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Daniel S. Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
| | - Piotr Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, California, USA
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Schulz A, Otton J, Hussain T, Miah T, Schuster A. Clinical Advances in Cardiovascular Computed Tomography: From Present Applications to Promising Developments. Curr Cardiol Rep 2024; 26:1063-1076. [PMID: 39162955 PMCID: PMC11461626 DOI: 10.1007/s11886-024-02110-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/26/2024] [Indexed: 08/21/2024]
Abstract
PURPOSE OF THE REVIEW This review aims to provide a profound overview on most recent studies on the clinical significance of Cardiovascular Computed Tomography (CCT) in diagnostic and therapeutic pathways. Herby, this review helps to pave the way for a more extended but yet purposefully use in modern day cardiovascular medicine. RECENT FINDINGS In recent years, new clinical applications of CCT have emerged. Major applications include the assessment of coronary artery disease and structural heart disease, with corresponding recommendations by major guidelines of international societies. While CCT already allows for a rapid and non-invasive diagnosis, technical improvements enable further in-depth assessments using novel imaging parameters with high temporal and spatial resolution. Those developments facilitate diagnostic and therapeutic decision-making as well as improved prognostication. This review determined that recent advancements in both hardware and software components of CCT allow for highly advanced examinations with little radiation exposure. This particularly strengthens its role in preventive care and coronary artery disease. The addition of functional analyses within and beyond coronary artery disease offers solutions in wide-ranging patient populations. Many techniques still require improvement and validation, however, CCT possesses potential to become a "one-stop-shop" examination.
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Affiliation(s)
- Alexander Schulz
- Department of Cardiology and Pneumology, Georg-August University, University Medical Center, Göttingen, Germany
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, 330 Brookline Ave, Boston, MA, 02215, USA
| | - James Otton
- South Western Sydney Clinical School, University of New South Wales, Sydney, NSW, Australia
| | - Tarique Hussain
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Departments of Paediatrics, Southwestern Medical Center, University of Texas, Dallas, TX, USA
| | - Tayaba Miah
- Division of Imaging Sciences and Biomedical Engineering, King's College London, London, UK
- Departments of Paediatrics, Southwestern Medical Center, University of Texas, Dallas, TX, USA
| | - Andreas Schuster
- Department of Cardiology and Pneumology, Georg-August University, University Medical Center, Göttingen, Germany.
- FORUM Cardiology, Rosdorf, Germany.
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Bhattaru A, Rojulpote C, Vidula M, Duda J, Maclean MT, Swago S, Thompson E, Gee J, Pieretti J, Drachman B, Cohen A, Dorbala S, Bravo PE, Witschey WR. Deep learning approach for automated segmentation of myocardium using bone scintigraphy single-photon emission computed tomography/computed tomography in patients with suspected cardiac amyloidosis. J Nucl Cardiol 2024; 33:101809. [PMID: 38307160 DOI: 10.1016/j.nuclcard.2024.101809] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/01/2023] [Revised: 08/02/2023] [Accepted: 08/02/2023] [Indexed: 02/04/2024]
Abstract
BACKGROUND We employed deep learning to automatically detect myocardial bone-seeking uptake as a marker of transthyretin cardiac amyloid cardiomyopathy (ATTR-CM) in patients undergoing 99mTc-pyrophosphate (PYP) or hydroxydiphosphonate (HDP) single-photon emission computed tomography (SPECT)/computed tomography (CT). METHODS We identified a primary cohort of 77 subjects at Brigham and Women's Hospital and a validation cohort of 93 consecutive patients imaged at the University of Pennsylvania who underwent SPECT/CT with PYP and HDP, respectively, for evaluation of ATTR-CM. Global heart regions of interest (ROIs) were traced on CT axial slices from the apex of the ventricle to the carina. Myocardial images were visually scored as grade 0 (no uptake), 1 (uptakeribs). A 2D U-net architecture was used to develop whole-heart segmentations for CT scans. Uptake was determined by calculating a heart-to-blood pool (HBP) ratio between the maximal counts value of the total heart region and the maximal counts value of the most superior ROI. RESULTS Deep learning and ground truth segmentations were comparable (p=0.63). A total of 42 (55%) patients had abnormal myocardial uptake on visual assessment. Automated quantification of the mean HBP ratio in the primary cohort was 3.1±1.4 versus 1.4±0.2 (p<0.01) for patients with positive and negative cardiac uptake, respectively. The model had 100% accuracy in the primary cohort and 98% in the validation cohort. CONCLUSION We have developed a highly accurate diagnostic tool for automatically segmenting and identifying myocardial uptake suggestive of ATTR-CM.
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Affiliation(s)
- Abhijit Bhattaru
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Medicine, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Chaitanya Rojulpote
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Mahesh Vidula
- Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Jeffrey Duda
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Matthew T Maclean
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sophia Swago
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Elizabeth Thompson
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James Gee
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Janice Pieretti
- Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Brian Drachman
- Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Adam Cohen
- Department of Oncology, University of Pennsylvania, Philadelphia, PA, USA
| | - Sharmila Dorbala
- Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA
| | - Paco E Bravo
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA; Department of Cardiology, University of Pennsylvania, Philadelphia, PA, USA.
| | - Walter R Witschey
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
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8
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Han D, Shanbhag A, Miller RJH, Kwok N, Waechter P, Builoff V, Newby DE, Dey D, Berman DS, Slomka P. Artificial intelligence-based automated left ventricular mass quantification from non-contrast cardiac CT scans: correlation with contrast CT and cardiac MRI. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.12.24301169. [PMID: 38260634 PMCID: PMC10802664 DOI: 10.1101/2024.01.12.24301169] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Non-contrast CT scans are not used for evaluating left ventricle myocardial mass (LV mass), which is typically evaluated with contrast CT or cardiovascular magnetic resonance imaging (MRI). We assessed the feasibility of LV mass estimation from standard, ECG-gated, non-contrast CT using an artificial intelligence (AI) approach and compare it with coronary CT angiography (CTA) and cardiac MRI. Methods We enrolled consecutive patients who underwent coronary CTA, which included non-contrast CT calcium scanning and contrast CTA, and cardiac MRI. The median interval between coronary CTA and MRI was 22 days (IQR: 3-76). We utilized an nn-Unet AI model that automatically segmented non-contrast CT structures. AI measurement of LV mass was compared to contrast CTA and MRI. Results A total of 316 patients (Age: 57.1±16.7, 56% male) were included. The AI segmentation took on average 22 seconds per case. An excellent correlation was observed between AI and contrast CTA LV mass measures (r=0.84, p<0.001), with no significant differences (136.5±55.3 vs. 139.6±56.9 g, p=0.133). Bland-Altman analysis showed minimal bias of 2.9. When compared to MRI, measured LV mass was higher with AI (136.5±55.3 vs. 127.1±53.1 g, p<0.001). There was an excellent correlation between AI and MRI (r=0.85, p<0.001), with a small bias (-9.4). There were no statistical differences between the correlations of LV mass between contrast CTA and MRI, or AI and MRI. Conclusions The AI-based automated estimation of LV mass from non-contrast CT demonstrated excellent correlations and minimal biases when compared to contrast CTA and MRI.
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Affiliation(s)
- Donghee Han
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Aakash Shanbhag
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Signal and Image Processing Institute, Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, CA, USA
| | - Robert JH Miller
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Cardiac Sciences, University of Calgary, Calgary AB, Canada
| | - Nicholas Kwok
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Parker Waechter
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Valerie Builoff
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - David E Newby
- British Heart Foundation Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, United Kingdom
| | - Damini Dey
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Daniel S Berman
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Piotr Slomka
- Departments of Medicine (Division of Artificial Intelligence in Medicine), Imaging and Biomedical Sciences Cedars-Sinai Medical Center, Los Angeles, CA, USA
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Szugye NA, Mahalingam N, Somasundaram E, Villa C, Segala J, Segala M, Zafar F, Morales DLS, Moore RA. Deep Learning for Automated Measurement of Total Cardiac Volume for Heart Transplantation Size Matching. RESEARCH SQUARE 2023:rs.3.rs-3788726. [PMID: 38234758 PMCID: PMC10793494 DOI: 10.21203/rs.3.rs-3788726/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2024]
Abstract
Background Total Cardiac Volume (TCV) based size matching using Computed Tomography (CT) is a novel technique to compare donor and recipient heart size in pediatric heart transplant that may increase overall utilization of available grafts. TCV requires manual segmentation, which limits its widespread use due to time and specialized software and training needed for segmentation. Objective This study aims to determine the accuracy of a Deep Learning (DL) approach using 3-dimensional Convolutional Neural Networks (3D-CNN) to calculate TCV, with the clinical aim of enabling fast and accurate TCV use at all transplant centers. Materials and Methods Ground truth TCV was segmented on CT scans of subjects aged 0-30 years, identified retrospectively. Ground truth segmentation masks were used to train and test a custom 3D-CNN model consisting of a Dense-Net architecture in combination with residual blocks of ResNet architecture. Results The model was trained on a cohort of 270 subjects and a validation cohort of 44 subjects (36 normal, 8 heart disease retained for model testing). The average Dice similarity coefficient of the validation cohort was 0.94 ± 0.03 (range 0.84-0.97). The mean absolute percent error of TCV estimation was 5.5%. There is no significant association between model accuracy and subject age, weight, or height. DL-TCV was on average more accurate for normal hearts than those listed for transplant (mean absolute percent error 4.5 ± 3.9 vs. 10.5 ± 8.5, p = 0.08). Conclusion A deep learning based 3D-CNN model can provide accurate automatic measurement of TCV from CT images.
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Affiliation(s)
| | | | | | - Chet Villa
- Cincinnati Children's Hospital Medical Center
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10
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Aquino GJ, Chamberlin J, Mercer M, Kocher M, Kabakus I, Akkaya S, Fiegel M, Brady S, Leaphart N, Dippre A, Giovagnoli V, Yacoub B, Jacob A, Gulsun MA, Sahbaee P, Sharma P, Waltz J, Schoepf UJ, Baruah D, Emrich T, Zimmerman S, Field ME, Agha AM, Burt JR. Deep learning model to quantify left atrium volume on routine non-contrast chest CT and predict adverse outcomes. J Cardiovasc Comput Tomogr 2021; 16:245-253. [PMID: 34969636 DOI: 10.1016/j.jcct.2021.12.005] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/15/2021] [Revised: 11/16/2021] [Accepted: 12/13/2021] [Indexed: 11/28/2022]
Abstract
BACKGROUND Low-dose computed tomography (LDCT) are performed routinely for lung cancer screening. However, a large amount of nonpulmonary data from these scans remains unassessed. We aimed to validate a deep learning model to automatically segment and measure left atrial (LA) volumes from routine NCCT and evaluate prediction of cardiovascular outcomes. METHODS We retrospectively evaluated 273 patients (median age 69 years, 55.5% male) who underwent LDCT for lung cancer screening. LA volumes were quantified by three expert cardiothoracic radiologists and a prototype AI algorithm. LA volumes were then indexed to the body surface area (BSA). Expert and AI LA volume index (LAVi) were compared and used to predict cardiovascular outcomes within five years. Logistic regression with appropriate univariate statistics were used for modelling outcomes. RESULTS There was excellent correlation between AI and expert results with an LAV intraclass correlation of 0.950 (0.936-0.960). Bland-Altman plot demonstrated the AI underestimated LAVi by a mean 5.86 mL/m2. AI-LAVi was associated with new-onset atrial fibrillation (AUC 0.86; OR 1.12, 95% CI 1.08-1.18, p < 0.001), HF hospitalization (AUC 0.90; OR 1.07, 95% CI 1.04-1.13, p < 0.001), and MACCE (AUC 0.68; OR 1.04, 95% CI 1.01-1.07, p = 0.01). CONCLUSION This novel deep learning algorithm for automated measurement of LA volume on lung cancer screening scans had excellent agreement with manual quantification. AI-LAVi is significantly associated with increased risk of new-onset atrial fibrillation, HF hospitalization, and major adverse cardiac and cerebrovascular events within 5 years.
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Affiliation(s)
- Gilberto J Aquino
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Jordan Chamberlin
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Megan Mercer
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Madison Kocher
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Ismail Kabakus
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Selcuk Akkaya
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Matthew Fiegel
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Sean Brady
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Nathan Leaphart
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Andrew Dippre
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Vincent Giovagnoli
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Basel Yacoub
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | | | | | | | | | - Jeffrey Waltz
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - U Joseph Schoepf
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Dhiraj Baruah
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Tilman Emrich
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA
| | - Stefan Zimmerman
- Johns Hopkins Hospital, Department of Radiology and Radiological Science, USA
| | - Michael E Field
- Medical University of South Carolina, Department of Medicine, USA
| | - Ali M Agha
- Baylor College of Medicine, Department of Medicine, USA
| | - Jeremy R Burt
- Medical University of South Carolina, Department of Radiology and Radiological Science, USA.
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11
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Hodges PW, Bailey JF, Fortin M, Battié MC. Paraspinal muscle imaging measurements for common spinal disorders: review and consensus-based recommendations from the ISSLS degenerative spinal phenotypes group. EUROPEAN SPINE JOURNAL : OFFICIAL PUBLICATION OF THE EUROPEAN SPINE SOCIETY, THE EUROPEAN SPINAL DEFORMITY SOCIETY, AND THE EUROPEAN SECTION OF THE CERVICAL SPINE RESEARCH SOCIETY 2021; 30:3428-3441. [PMID: 34542672 DOI: 10.1007/s00586-021-06990-2] [Citation(s) in RCA: 42] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 08/12/2021] [Accepted: 09/05/2021] [Indexed: 12/17/2022]
Abstract
PURPOSE Paraspinal muscle imaging is of growing interest related to improved phenotyping, prognosis, and treatment of common spinal disorders. We reviewed issues related to paraspinal muscle imaging measurement that contribute to inconsistent findings between studies and impede understanding. METHODS Three key contributors to inconsistencies among studies of paraspinal muscle imaging measurements were reviewed: failure to consider possible mechanisms underlying changes in paraspinal muscles, lack of control of confounding factors, and variations in spinal muscle imaging modalities and measurement protocols. Recommendations are provided to address these issues to improve the quality and coherence of future research. RESULTS Possible pathophysiological responses of paraspinal muscle to various common spinal disorders in acute or chronic phases are often overlooked, yet have important implications for the timing, distribution, and nature of changes in paraspinal muscle. These considerations, as well as adjustment for possible confounding factors, such as sex, age, and physical activity must be considered when planning and interpreting paraspinal muscle measurements in studies of spinal conditions. Adoption of standardised imaging measurement protocols for paraspinal muscle morphology and composition, considering the strengths and limitations of various imaging modalities, is critically important to interpretation and synthesis of research. CONCLUSION Study designs that consider physiological and pathophysiological responses of muscle, adjust for possible confounding factors, and use common, standardised measures are needed to advance knowledge of the determinants of variations or changes in paraspinal muscle and their influence on spinal health.
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Affiliation(s)
- Paul W Hodges
- School of Health and Rehabilitation Sciences, The University of Queensland, Brisbane, QLD, 4072, Australia.
| | - Jeannie F Bailey
- Department of Orthopedic Surgery, University of California, San Francisco, CA, USA
| | - Maryse Fortin
- Department of Health, Kinesiology & Applied Physiology, Concordia University, Montreal, QC, Canada
| | - Michele C Battié
- Faculty of Health Sciences and Western's Bone and Joint Institute, Western University, London, ON, Canada
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12
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Lartaud PJ, Dupont C, Hallé D, Schleef A, Dessouky R, Vlachomitrou AS, Rouet JM, Nempont O, Boussel L. A conventional-to-spectral CT image translation augmentation workflow for robust contrast injection-independent organ segmentation. Med Phys 2021; 49:1108-1122. [PMID: 34689353 DOI: 10.1002/mp.15310] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/07/2021] [Accepted: 10/11/2021] [Indexed: 11/07/2022] Open
Abstract
PURPOSE In cardiovascular imaging, the numerous contrast injection protocols used to enhance structures make it difficult to gather training datasets for deep learning applications supporting diverse protocols. Moreover, creating annotations on non-contrast scans is extremely tedious. Recently, spectral CT's virtual-non-contrast images (VNC) have been used as data augmentation to train segmentation networks performing on enhanced and true-non-contrast (TNC) scans alike, while improving results on protocols absent of their training dataset. However, spectral data are not widely available, making it difficult to gather specific datasets for each task. As a solution, we present a data augmentation workflow based on a trained image translation network, to bring spectral-like augmentation to any conventional CT dataset. METHOD The HU-to-spectral image translation network (HUSpecNet) was first trained to generate VNC from HU images, using an unannotated spectral dataset of 1830 patients. It was then tested on a second dataset of 300 spectral CT scans, by comparing generated VNC (VNCDL ) to their true counterparts. To illustrate and compare our workflow's efficiency with true spectral augmentation, HUSpecNet was applied to a third dataset of 112 spectral scans to generate VNCDL along HU and VNC images. Three different 3D networks (U-Net, X-Net, U-Net++) were trained for multi-label heart segmentation, following four augmentation strategies. As baselines, trainings were performed on contrasted images without (HUonly) and with conventional gray-values augmentation (HUaug). Then, the same networks were trained using a proportion of contrasted and VNC/VNCDL images (TrueSpec/GenSpec). Each training strategy applied to each architecture was evaluated using Dice coefficients on a fourth multi-centric multi-vendor single-energy CT dataset of 121 patients, including different contrast injection protocols and unenhanced scans. The U-Net++ results were further explored with distance metrics on every label. RESULTS Tested on 300 full scans, our HUSpectNet translation network shows a mean absolute error of 6.70±2.83 HU between VNCDL and VNC, while peak-signal-to-noise-ratio reaches 43.89 dB. GenSpec and TrueSpec show very close results regardless of the protocol and used architecture: mean Dice coefficients (DSCmean ) are equal with a margin of 0.006, ranging from 0.879 to 0.938. Their performances significantly increase on TNC scans (p-values<0.017 for all architectures) compared to HUonly and HUaug, with DSCmean of 0.448/0.770/0.879/0.885 for HUonly/HUaug/TrueSpec/GenSpec using the Unet++ architecture. Significant improvements are also noted for all architectures on chest-abdominal-pelvic scans (p-values<0.007) compared to HUonly and for pulmonary embolism scans (p-values<0.039) compared to HUaug. Using Unet++, DSCmean reaches 0.892/0.901/0.903 for HUonly/TrueSpec/GenSpec on pulmonary embolism scans and 0.872/0.896/0.896 for HUonly/TrueSpec/GenSpec on chest-abdominal-pelvic scans. CONCLUSION Using the proposed workflow, we trained versatile heart segmentation networks on a dataset of conventional enhanced CT scans, providing robust predictions on both enhanced scans with different contrast injection protocols and TNC scans. The performances obtained were not significantly inferior to training the model on a genuine spectral CT dataset, regardless of the architecture implemented. Using a general-purpose conventional-to-spectral CT translation network as data augmentation could therefore contribute to reducing data collection and annotation requirements for machine learning-based CT studies, while extending their range of application. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Pierre-Jean Lartaud
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France
- Philips Research France, Suresnes, France
| | | | | | | | - Riham Dessouky
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France
- Radiology Department, Faculty of Medicine, Zagazig University, Zagazig, Egypt
| | | | | | | | - Loïc Boussel
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France
- Hospices Civils de Lyon, Lyon, France
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13
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Lartaud PJ, Hallé D, Schleef A, Dessouky R, Vlachomitrou AS, Douek P, Rouet JM, Nempont O, Boussel L. Spectral augmentation for heart chambers segmentation on conventional contrasted and unenhanced CT scans: an in-depth study. Int J Comput Assist Radiol Surg 2021; 16:1699-1709. [PMID: 34363582 DOI: 10.1007/s11548-021-02468-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2021] [Accepted: 07/23/2021] [Indexed: 01/26/2023]
Abstract
PURPOSE Recently, machine learning has outperformed established tools for automated segmentation in medical imaging. However, segmentation of cardiac chambers still proves challenging due to the variety of contrast agent injection protocols used in clinical practice, inducing disparities of contrast between cavities. Hence, training a generalist network requires large training datasets representative of these protocols. Furthermore, segmentation on unenhanced CT scans is further hindered by the challenge of obtaining ground truths from these images. Newly available spectral CT scanners allow innovative image reconstructions such as virtual non-contrast (VNC) imaging, mimicking non-contrasted conventional CT studies from a contrasted scan. Recent publications have demonstrated that networks can be trained using VNC to segment contrasted and unenhanced conventional CT scans to reduce annotated data requirements and the need for annotations on unenhanced scans. We propose an extensive evaluation of this statement. METHOD We undertake multiple trainings of a 3D multi-label heart segmentation network with (HU-VNC) and without (HUonly) VNC as augmentation, using decreasing training dataset sizes (114, 76, 57, 38, 29, 19 patients). At each step, both networks are tested on a multi-vendor, multi-centric dataset of 122 patients, including different protocols: pulmonary embolism (PE), chest-abdomen-pelvis (CAP), heart CT angiography (CTA) and true non-contrast scans (TNC). An in-depth comparison of resulting Dice coefficients and distance metrics is performed for the networks trained on the largest dataset. RESULTS HU-VNC-trained on 57 patients significantly outperforms HUonly trained on 114 regarding CAP and TNC scans (mean Dice coefficients of 0.881/0.835 and 0.882/0.416, respectively). When trained on the largest dataset, significant improvements in all labels are noted for TNC and CAP scans (mean Dice coefficient of 0.882/0.416 and 0.891/0.835, respectively). CONCLUSION Adding VNC images as training augmentation allows the network to perform on unenhanced scans and improves segmentations on other imaging protocols, while using a reduced training dataset.
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Affiliation(s)
- Pierre-Jean Lartaud
- Philips Research France, Suresnes, France. .,CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France.
| | | | | | - Riham Dessouky
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France
| | | | - Philippe Douek
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France.,Hospices Civils de Lyon, Lyon, France
| | | | | | - Loïc Boussel
- CREATIS UMR5220, INSERM U1044, INSA, Université de Lyon, Lyon, France.,Hospices Civils de Lyon, Lyon, France
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Loap P, Tkatchenko N, Goudjil F, Ribeiro M, Baron B, Fourquet A, Kirova Y. Cardiac substructure exposure in breast radiotherapy: a comparison between intensity modulated proton therapy and volumetric modulated arc therapy. Acta Oncol 2021; 60:1038-1044. [PMID: 33788665 DOI: 10.1080/0284186x.2021.1907860] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
INTRODUCTION Proton therapy for breast cancer treatment reduces cardiac radiation exposure. Left-sided breast cancer patients with indication for internal mammary chain (IMC) irradiation are most at risk of radiation-induced cardiotoxicity. This study aims to evaluate in this situation the potential dosimetric benefit of intensity modulated proton therapy (IMPT) over volumetric modulated arc therapy (VMAT) at the cardiac substructure level. MATERIALS AND METHODS Cardiac substructures were retrospectively delineated according to ESTRO guidelines on the simulation CT scans of fourteen left-sided breast cancer patients having undergone conserving surgery and adjuvant locoregional free-breathing (FB-) or deep inspiration breath-hold (DIBH-) VMAT with internal mammary chain irradiation. IMPT treatment was re-planned on the simulation CT scans. Mean doses to cardiac substructures were retrieved and compared between VMAT treatment plans and IMPT simulation plans. Pearson correlation coefficients were calculated between mean doses delivered to cardiac substructures using these two techniques. RESULTS Mean doses to all cardiac substructures were significantly lower with IMPT than with VMAT. Regardless of the irradiation technique, the most exposed cardiac substructure was the mid segment of the left anterior descending coronary artery (LADCA). Pearson correlation coefficients between mean doses to cardiac substructures were usually weak and statistically non-significant for IMPT; mean heart dose (MHD) only correlated with mean doses delivered to the right ventricle, to the mid segment of the right coronary artery (RCA) and, to a lesser extent, to the LADCA. CONCLUSION The dosimetric benefit of IMPT over conformal photon therapy was consistently observed for all cardiac substructures. MHD may not be a reliable dosimetric parameter for precise cardiac exposure evaluation when planning IMPT.
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Affiliation(s)
- Pierre Loap
- Institut Curie, Department of Radiation Oncology, Paris, France
| | | | - Farid Goudjil
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Madison Ribeiro
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Brian Baron
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Alain Fourquet
- Institut Curie, Department of Radiation Oncology, Paris, France
| | - Youlia Kirova
- Institut Curie, Department of Radiation Oncology, Paris, France
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15
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Farrugia M, Yu H, Singh AK, Malhotra H. Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images. World J Clin Oncol 2021; 12:95-102. [PMID: 33680876 PMCID: PMC7918522 DOI: 10.5306/wjco.v12.i2.95] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Revised: 12/07/2020] [Accepted: 12/22/2020] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality, yet definition of these structures is labor intensive and not standard. Autosegmentation software may potentially address these issues, however it is unclear whether this approach can be broadly applied across different treatment planning conditions. We investigated the feasibility of autosegmentation of the cardiac substructures in four-dimensional (4D) computed tomography (CT), respiratory-gated, non-contrasted imaging. AIM To determine whether autosegmentation can be successfully employed on 4DCT respiratory-gated, non-contrasted imaging. METHODS We included patients who underwent stereotactic body radiation therapy for inoperable, early-stage non-small cell lung cancer from 2007 to 2019. All patients were simulated via 4DCT imaging with respiratory gating without intravenous contrast. Generated structure quality was evaluated by degree of required manual edits and volume discrepancy between the autocontoured structures and its edited sister structure. RESULTS Initial 17-structure cardiac atlas was generated with 20 patients followed by three successive iterations of 10 patients using MIM software. The great vessels and heart chambers were reliably autosegmented with most edits considered minor. In contrast, coronary arteries either failed to be autosegmented or the generated structures required major alterations necessitating deletion and manual definition. Similarly, the generated mitral and tricuspid valves were poor whereas the aortic and pulmonary valves required at least minor and moderate changes respectively. For the majority of subsites, the additional samples did not appear to substantially impact the quality of generated structures. Volumetric analysis between autosegmented and its manually edited sister structure yielded comparable findings to the physician-based assessment of structure quality. CONCLUSION The use of MIM software with 30-sample subject library was found to be useful in delineating many of the heart substructures with acceptable clinical accuracy on respiratory-gated 4DCT imaging. Small volume structures, such as the coronary arteries were poorly autosegmented and require manual definition.
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Affiliation(s)
- Mark Farrugia
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States
| | - Han Yu
- Department of Biostatistics and Bioinformatics, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States
| | - Anurag K Singh
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States
| | - Harish Malhotra
- Department of Radiation Medicine, Roswell Park Comprehensive Cancer Center, Buffalo, NY 14203, United States
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16
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Loap P, Tkatchenko N, Kirova Y. Evaluation of a delineation software for cardiac atlas-based autosegmentation: An example of the use of artificial intelligence in modern radiotherapy. Cancer Radiother 2020; 24:826-833. [PMID: 33144062 DOI: 10.1016/j.canrad.2020.04.012] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2020] [Revised: 04/15/2020] [Accepted: 04/23/2020] [Indexed: 12/13/2022]
Abstract
PURPOSE The primary objective of this work was to implement and evaluate a cardiac atlas-based autosegmentation technique based on the "Workflow Box" software (Mirada Medical, Oxford UK), in order to delineate cardiac substructures according to European Society of Therapeutic Radiation Oncology (ESTRO) guidelines; review and comparison with other cardiac atlas-based autosegmentation algorithms published to date. MATERIALS AND METHODS Of an atlas of data set from 20 breast cancer patients' CT scans with recontoured cardiac substructures creation according to the ESTRO guidelines. Performance evaluation on a validation data set consisting of 20 others CT scans acquired in the same treatment position: cardiac substructure were automatically contoured by the Mirada system, using the implemented cardiac atlas, and simultaneously manually contoured by a radiation oncologist. The Dice similarity coefficient was used to evaluate the concordance level between the manual and the automatic segmentations. RESULTS Dice similarity coefficient value was 0.95 for the whole heart and 0.80 for the four cardiac chambers. Average Dice similarity coefficient value for the left ventricle walls was 0.50, ranging between 0.34 for the apical wall and 0.70 for the lateral wall. Compared to manual contours, autosegmented substructure volumes were significantly smaller, with the exception of the left ventricle. Coronary artery segmentation was unsuccessful. Performances were overall similar to other published cardiac atlas-based autosegmentation algorithms. CONCLUSION The evaluated cardiac atlas-based autosegmentation technique, using the Mirada software, demonstrated acceptable performance for cardiac cavities delineation. However, algorithm improvement is still needed in order to develop efficient and trusted cardiac autosegmentation working tools for daily practice.
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Affiliation(s)
- P Loap
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France.
| | - N Tkatchenko
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France
| | - Y Kirova
- Department of radiation oncology, institut Curie, 26, rue d'Ulm, 75006 Paris, France
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17
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Bruns S, Wolterink JM, Takx RAP, Hamersvelt RW, Suchá D, Viergever MA, Leiner T, Išgum I. Deep learning from dual‐energy information for whole‐heart segmentation in dual‐energy and single‐energy non‐contrast‐enhanced cardiac CT. Med Phys 2020; 47:5048-5060. [DOI: 10.1002/mp.14451] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2020] [Revised: 07/27/2020] [Accepted: 08/03/2020] [Indexed: 11/11/2022] Open
Affiliation(s)
- Steffen Bruns
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
| | - Jelmer M. Wolterink
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
| | - Richard A. P. Takx
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Robbert W. Hamersvelt
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Dominika Suchá
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Max A. Viergever
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Tim Leiner
- Department of Radiology University Medical Center Utrecht Utrecht3584 CX Netherlands
| | - Ivana Išgum
- Department of Biomedical Engineering and Physics Amsterdam UMC – location AMCUniversity of Amsterdam Amsterdam1105 AZ Netherlands
- Image Sciences Institute University Medical Center Utrecht Utrecht3584 CX Netherlands
- Amsterdam Cardiovascular SciencesAmsterdam UMC Amsterdam1105 AZ Netherlands
- Department of Radiology and Nuclear Medicine Amsterdam UMC – location AMC Amsterdam1105 AZ Netherlands
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van den Oever LB, Vonder M, van Assen M, van Ooijen PMA, de Bock GH, Xie XQ, Vliegenthart R. Application of artificial intelligence in cardiac CT: From basics to clinical practice. Eur J Radiol 2020; 128:108969. [PMID: 32361380 DOI: 10.1016/j.ejrad.2020.108969] [Citation(s) in RCA: 22] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2019] [Revised: 01/30/2020] [Accepted: 03/11/2020] [Indexed: 12/18/2022]
Abstract
Research into the possibilities of AI in cardiac CT has been growing rapidly in the last decade. With the rise of publicly available databases and AI algorithms, many researchers and clinicians have started investigations into the use of AI in the clinical workflow. This review is a comprehensive overview on the types of tasks and applications in which AI can aid the clinician in cardiac CT, and can be used as a primer for medical researchers starting in the field of AI. The applications of AI algorithms are explained and recent examples in cardiac CT of these algorithms are further elaborated on. The critical factors for implementation in the future are discussed.
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Affiliation(s)
- L B van den Oever
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, the Netherlands
| | - M Vonder
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, the Netherlands
| | - M van Assen
- University of Groningen, University Medical Center Groningen, Faculty of Medicine, Groningen, the Netherlands; Divisions of Cardiothoracic Imaging, Nuclear Medicine and Molecular Imaging, Department of Radiology and Imaging Sciences, Emory University Hospital, Atlanta, GA, USA
| | - P M A van Ooijen
- University of Groningen, University Medical Center Groningen, Department of Radiation Oncology, the Netherlands
| | - G H de Bock
- University of Groningen, University Medical Center Groningen, Department of Epidemiology, the Netherlands
| | - X Q Xie
- Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Department of Radiology, Shanghai, The People's Republic of China
| | - R Vliegenthart
- University of Groningen, University Medical Center Groningen, Department of Radiology, the Netherlands.
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Bui V, Shanbhag SM, Levine O, Jacobs M, Bandettini WP, Chang LC, Chen MY, Hsu LY. Simultaneous Multi-Structure Segmentation of the Heart and Peripheral Tissues in Contrast Enhanced Cardiac Computed Tomography Angiography. IEEE ACCESS : PRACTICAL INNOVATIONS, OPEN SOLUTIONS 2020; 8:16187-16202. [PMID: 33747668 PMCID: PMC7971052 DOI: 10.1109/access.2020.2966985] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Contrast enhanced cardiac computed tomography angiography (CTA) is a prominent imaging modality for diagnosing cardiovascular diseases non-invasively. It assists the evaluation of the coronary artery patency and provides a comprehensive assessment of structural features of the heart and great vessels. However, physicians are often required to evaluate different cardiac structures and measure their size manually. Such task is very time-consuming and tedious due to the large number of image slices in 3D data. We present a fully automatic method based on a combined multi-atlas and corrective segmentation approach to label the heart and its associated cardiovascular structures. This method also automatically separates other surrounding intrathoracic structures from CTA images. Quantitative assessment of the proposed method is performed on 36 studies with a reference standard obtained from expert manual segmentation of various cardiac structures. Qualitative evaluation is also performed by expert readers to score 120 studies of the automatic segmentation. The quantitative results showed an overall Dice of 0.93, Hausdorff distance of 7.94 mm, and mean surface distance of 1.03 mm between automatically and manually segmented cardiac structures. The visual assessment also attained an excellent score for the automatic segmentation. The average processing time was 2.79 minutes. Our results indicate the proposed automatic framework significantly improves accuracy and computational speed in conventional multi-atlas based approach, and it provides comprehensive and reliable multi-structural segmentation of CTA images that is valuable for clinical application.
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Affiliation(s)
- Vy Bui
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA
| | - Sujata M. Shanbhag
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Oscar Levine
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Washington University in St. Louis, St. Louis, MO, USA
| | - Matthew Jacobs
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA
| | - W. Patricia Bandettini
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Lin-Ching Chang
- Department of Electrical Engineering and Computer Science, Catholic University of America, Washington DC, USA
| | - Marcus Y. Chen
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Li-Yueh Hsu
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
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Finnegan R, Dowling J, Koh ES, Tang S, Otton J, Delaney G, Batumalai V, Luo C, Atluri P, Satchithanandha A, Thwaites D, Holloway L. Feasibility of multi-atlas cardiac segmentation from thoracic planning CT in a probabilistic framework. Phys Med Biol 2019; 64:085006. [PMID: 30856618 DOI: 10.1088/1361-6560/ab0ea6] [Citation(s) in RCA: 41] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Toxicity to cardiac and coronary structures is an important late morbidity for patients undergoing left-sided breast radiotherapy. Many current studies have relied on estimates of cardiac doses assuming standardised anatomy, with a calculated increase in relative risk of 7.4% per Gy (mean heart dose). To provide individualised estimates for dose, delineation of various cardiac structures on patient images is required. Automatic multi-atlas based segmentation can provide a consistent, robust solution, however there are challenges to this method. We are aiming to develop and validate a cardiac atlas and segmentation framework, with a focus on the limitations and uncertainties in the process. We present a probabilistic approach to segmentation, which provides a simple method to incorporate inter-observer variation, as well as a useful tool for evaluating the accuracy and sources of error in segmentation. A dataset consisting of 20 planning computed tomography (CT) images of Australian breast cancer patients with delineations of 17 structures (including whole heart, four chambers, coronary arteries and valves) was manually contoured by three independent observers, following a protocol based on a published reference atlas, with verification by a cardiologist. To develop and validate the segmentation framework a leave-one-out cross-validation strategy was implemented. Performance of the automatic segmentations was evaluated relative to inter-observer variability in manually-derived contours; measures of volume and surface accuracy (Dice similarity coefficient (DSC) and mean absolute surface distance (MASD), respectively) were used to compare automatic segmentation to the consensus segmentation from manual contours. For the whole heart, the resulting segmentation achieved a DSC of [Formula: see text], with a MASD of [Formula: see text] mm. Quantitative results, together with the analysis of probabilistic labelling, indicate the feasibility of accurate and consistent segmentation of larger structures, whereas this is not the case for many smaller structures, where a major limitation in segmentation accuracy is the inter-observer variability in manual contouring.
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Affiliation(s)
- Robert Finnegan
- School of Physics, Institute of Medical Physics, University of Sydney, Sydney, Australia. Ingham Institute for Applied Medical Research, Liverpool, Australia. Author to whom all correspondence should be addressed
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Dahiya N, Yezzi A, Piccinelli M, Garcia E. Integrated 3D Anatomical Model for Automatic Myocardial Segmentation in Cardiac CT Imagery. COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING-IMAGING AND VISUALIZATION 2019; 7:690-706. [PMID: 31890358 DOI: 10.1080/21681163.2019.1583607] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
Segmentation of epicardial and endocardial boundaries is a critical step in diagnosing cardiovascular function in heart patients. The manual tracing of organ contours in Computed Tomography Angiography (CTA) slices is subjective, time-consuming and impractical in clinical setting. We propose a novel multi-dimensional automatic edge detection algorithm based on shape priors and principal component analysis (PCA). We have developed a highly customized parametric model for implicit representations of segmenting curves (3D) for Left Ventricle (LV), Right Ventricle (RV), and Epicardium (Epi) used simultaneously to achieve myocardial segmentation. We have combined these representations in a region-based image modeling framework with high level constraints enabling the modeling of complex cardiac anatomical structures to automatically guide the segmentation of endo/epicardial boundaries. Test results on 30 short-axis CTA datasets show robust segmentation with error (mean ± std mm) of (1.46 ± 0.41), (2.06 ± 0.65), (2.88 ± 0.59) for LV, RV and Epi respectively.
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Affiliation(s)
- N Dahiya
- Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, USA
| | - A Yezzi
- Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, USA
| | - M Piccinelli
- Emory University School of Medicine, 101 Woodruff Circle, Atlanta, GA, 30322, USA
| | - E Garcia
- Emory University School of Medicine, 101 Woodruff Circle, Atlanta, GA, 30322, USA
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22
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Quantitative morphometric analysis of adult teleost fish by X-ray computed tomography. Sci Rep 2018; 8:16531. [PMID: 30410001 PMCID: PMC6224569 DOI: 10.1038/s41598-018-34848-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 10/22/2018] [Indexed: 12/11/2022] Open
Abstract
Vertebrate models provide indispensable paradigms to study development and disease. Their analysis requires a quantitative morphometric study of the body, organs and tissues. This is often impeded by pigmentation and sample size. X-ray micro-computed tomography (micro-CT) allows high-resolution volumetric tissue analysis, largely independent of sample size and transparency to visual light. Importantly, micro-CT data are inherently quantitative. We report a complete pipeline of high-throughput 3D data acquisition and image analysis, including tissue preparation and contrast enhancement for micro-CT imaging down to cellular resolution, automated data processing and organ or tissue segmentation that is applicable to comparative 3D morphometrics of small vertebrates. Applied to medaka fish, we first create an annotated anatomical atlas of the entire body, including inner organs as a quantitative morphological description of an adult individual. This atlas serves as a reference model for comparative studies. Using isogenic medaka strains we show that comparative 3D morphometrics of individuals permits identification of quantitative strain-specific traits. Thus, our pipeline enables high resolution morphological analysis as a basis for genotype-phenotype association studies of complex genetic traits in vertebrates.
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Morais P, Vilaça JL, Queirós S, Marchi A, Bourier F, Deisenhofer I, D'hooge J, Tavares JMRS. Automated segmentation of the atrial region and fossa ovalis towards computer-aided planning of inter-atrial wall interventions. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2018; 161:73-84. [PMID: 29852969 DOI: 10.1016/j.cmpb.2018.04.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/21/2018] [Revised: 03/29/2018] [Accepted: 04/17/2018] [Indexed: 06/08/2023]
Abstract
BACKGROUND AND OBJECTIVE Image-fusion strategies have been applied to improve inter-atrial septal (IAS) wall minimally-invasive interventions. Hereto, several landmarks are initially identified on richly-detailed datasets throughout the planning stage and then combined with intra-operative images, enhancing the relevant structures and easing the procedure. Nevertheless, such planning is still performed manually, which is time-consuming and not necessarily reproducible, hampering its regular application. In this article, we present a novel automatic strategy to segment the atrial region (left/right atrium and aortic tract) and the fossa ovalis (FO). METHODS The method starts by initializing multiple 3D contours based on an atlas-based approach with global transforms only and refining them to the desired anatomy using a competitive segmentation strategy. The obtained contours are then applied to estimate the FO by evaluating both IAS wall thickness and the expected FO spatial location. RESULTS The proposed method was evaluated in 41 computed tomography datasets, by comparing the atrial region segmentation and FO estimation results against manually delineated contours. The automatic segmentation method presented a performance similar to the state-of-the-art techniques and a high feasibility, failing only in the segmentation of one aortic tract and of one right atrium. The FO estimation method presented an acceptable result in all the patients with a performance comparable to the inter-observer variability. Moreover, it was faster and fully user-interaction free. CONCLUSIONS Hence, the proposed method proved to be feasible to automatically segment the anatomical models for the planning of IAS wall interventions, making it exceptionally attractive for use in the clinical practice.
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Affiliation(s)
- Pedro Morais
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Portugal; Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium.
| | - João L Vilaça
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; 2Ai - Polytechnic Institute of Cávado and Ave, Barcelos, Portugal.
| | - Sandro Queirós
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; ICVS/3B's - PT Government Associate Laboratory, Braga/Guimarães, Portugal; Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium; Algoritmi Center, School of Engineering, University of Minho, Guimarães, Portugal.
| | - Alberto Marchi
- Cardiomyopathies Unit, Careggi University Hospital Florence, Italy
| | - Felix Bourier
- German Heart Center Munich, Technical University, Munich, Germany.
| | | | - Jan D'hooge
- Lab on Cardiovascular Imaging & Dynamics, Department of Cardiovascular Sciences, KULeuven - University of Leuven, Leuven, Belgium.
| | - João Manuel R S Tavares
- Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Portugal.
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24
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Bos D, Leening MJG. Leveraging the coronary calcium scan beyond the coronary calcium score. Eur Radiol 2018; 28:3082-3087. [PMID: 29383526 PMCID: PMC5986828 DOI: 10.1007/s00330-017-5264-3] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2017] [Revised: 11/28/2017] [Accepted: 12/20/2017] [Indexed: 12/21/2022]
Abstract
Non-contrast cardiac computed tomography in order to obtain the coronary artery calcium score has become an established diagnostic procedure in the clinical setting, and is commonly employed in clinical and population-based research. This state-of-the-art review paper highlights the potential gain in information that can be obtained from the non-contrast coronary calcium scans without any necessary modifications to the scan protocol. This includes markers of cardio-metabolic health, such as the amount of epicardial fat and liver fat, but also markers of general health including bone density and lung density. Finally, this paper addresses the importance of incidental findings and of radiation exposure accompanying imaging with non-contrast cardiac computed tomography. Despite the fact that coronary calcium scan protocols have been optimized for the visualization of coronary calcification in terms image quality and radiation exposure, it is important for radiologists, cardiologists and medical specialists in the field of preventive medicine to acknowledge that numerous additional markers of cardio-metabolic health and general health can be readily identified on a coronary calcium scan. KEY POINTS • The coronary artery calcium score substantially increased the use of cardiac CT. • Cardio-metabolic and general health markers may be derived without changes to the scan protocol. • Those include epicardial fat, aortic valve calcifications, liver fat, bone density, and lung density. • Clinicians must be aware of this potential additional yield from non-contrast cardiac CT.
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Affiliation(s)
- Daniel Bos
- Department of Radiology and Nuclear Medicine, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands. .,Department of Epidemiology, Erasmus MC - University Medical Centre Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands. .,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Maarten J G Leening
- Department of Epidemiology, Erasmus MC - University Medical Centre Rotterdam, P.O. Box 2040, 3000 CA, Rotterdam, The Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA.,Department of Cardiology, Erasmus MC - University Medical Centre Rotterdam, Rotterdam, The Netherlands
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