• computer modelling
• machine learning
• ageing

• Humans
• Male
• Female
• Aged
• Alzheimer Disease/diagnostic imaging
• Alzheimer Disease/pathology
• Middle Aged
• Brain/blood supply
• Brain/diagnostic imaging
• Magnetic Resonance Imaging
• Stroke/diagnostic imaging
• Adult
• Cerebrovascular Circulation/physiology
• Deep Learning
• Supervised Machine Learning
• Aged, 80 and over

• the Beijing Natural Science Foundation under Grant 4222007 the National Natural Science Foundation of China under Grant 62271016
• Natural Science Foundation of Xiamen, China under Grant 3502Z202373088 Fujian provincial health technology project under Grant 2023QNB020
• the National Key R&D Program of China under Grant 2022YFC2009900

• Magnetic Resonance Angiography/methods
• Algorithms

• Angiography
• First order derivative
• Intensity-curvature-functional
• Inverse Fourier transformation

• Algorithms
• Brain/diagnostic imaging
• Humans
• Image Processing, Computer-Assisted
• Magnetic Resonance Angiography

• Carotid arteries
• Convolutional neural network
• Magnetic resonance imaging
• Segmentation
• Vessel wall

• R01 HL128056 NHLBI NIH HHS

• cerebrovascular segmentation
• deep neural network
• hidden Markov random field model
• magnetic resonance angiography
• unsupervised learning

A robust method is presented for the segmentation of the full cerebral vasculature in 4-dimensional (4D) computed tomography (CT). The method consists of candidate vessel selection, feature extraction, random forest classification and postprocessing. Image features include among others the weighted temporal variance image and parameters, including entropy, of an intensity histogram in a local region at different scales. These histogram parameters revealed to be a strong feature in the detection of vessels regardless of shape and size. The method was trained and tested on a large database of 264 patients with suspicion of acute ischemia who underwent 4D CT in our hospital in the period January 2014 to December 2015. Five subvolumes representing different regions of the cerebral vasculature were annotated in each image in the training set by medical assistants. The evaluation was done on 242 patients. A total of 16 (<8%) patients showed severe under or over segmentation and were reported as failures. One out of five subvolumes was randomly annotated in 159 patients and was used for quantitative evaluation. Quantitative evaluation showed a Dice coefficient of 0.91 ± 0.07 and a modified Hausdorff distance of 0.23 ± 0.22 mm. Therefore, robust vessel segmentation in 4D CT is feasible with good accuracy.

• MR angiogram
• feature extraction
• intracranial artery
• open-curve active contour
• time-of-flight
• vessel tracing

• Graph cuts
• Height ridge traversal
• Leaf node line-growing (LNLG)
• Liver vessel segmentation
• Optimal oriented flux (OOF)
• Oriented flux antisymmetry (OFA)

• Algorithms
• Angiography
• Hepatic Veins/diagnostic imaging
• Humans
• Imaging, Three-Dimensional
• Liver/blood supply
• Liver/diagnostic imaging
• Portal Vein/drug effects
• Tomography, X-Ray Computed

• Image segmentation
• Non-uniform illumination
• Tubular structures

• R01 LM011415 NLM NIH HHS

Accurate segmentation of blood vessels plays an important role in the computer-aided diagnosis and interventional treatment of vascular diseases. The statistical method is an important component of effective vessel segmentation; however, several limitations discourage the segmentation effect, i.e., dependence of the image modality, uneven contrast media, bias field, and overlapping intensity distribution of the object and background. In addition, the mixture models of the statistical methods are constructed relaying on the characteristics of the image histograms. Thus, it is a challenging issue for the traditional methods to be available in vessel segmentation from multi-modality angiographic images.

To overcome these limitations, a flexible segmentation method with a fixed mixture model has been proposed for various angiography modalities. Our method mainly consists of three parts. Firstly, multi-scale filtering algorithm was used on the original images to enhance vessels and suppress noises. As a result, the filtered data achieved a new statistical characteristic. Secondly, a mixture model formed by three probabilistic distributions (two Exponential distributions and one Gaussian distribution) was built to fit the histogram curve of the filtered data, where the expectation maximization (EM) algorithm was used for parameters estimation. Finally, three-dimensional (3D) Markov random field (MRF) were employed to improve the accuracy of pixel-wise classification and posterior probability estimation. To quantitatively evaluate the performance of the proposed method, two phantoms simulating blood vessels with different tubular structures and noises have been devised. Meanwhile, four clinical angiographic data sets from different human organs have been used to qualitatively validate the method. To further test the performance, comparison tests between the proposed method and the traditional ones have been conducted on two different brain magnetic resonance angiography (MRA) data sets.

The results of the phantoms were satisfying, e.g., the noise was greatly suppressed, the percentages of the misclassified voxels, i.e., the segmentation error ratios, were no more than 0.3%, and the Dice similarity coefficients (DSCs) were above 94%. According to the opinions of clinical vascular specialists, the vessels in various data sets were extracted with high accuracy since complete vessel trees were extracted while lesser non-vessels and background were falsely classified as vessel. In the comparison experiments, the proposed method showed its superiority in accuracy and robustness for extracting vascular structures from multi-modality angiographic images with complicated background noises.

The experimental results demonstrated that our proposed method was available for various angiographic data. The main reason was that the constructed mixture probability model could unitarily classify vessel object from the multi-scale filtered data of various angiography images. The advantages of the proposed method lie in the following aspects: firstly, it can extract the vessels with poor angiography quality, since the multi-scale filtering algorithm can improve the vessel intensity in the circumstance such as uneven contrast media and bias field; secondly, it performed well for extracting the vessels in multi-modality angiographic images despite various signal-noises; and thirdly, it was implemented with better accuracy, and robustness than the traditional methods. Generally, these traits declare that the proposed method would have significant clinical application.

• Markov random field
• Multi-modality angiographic images
• Multi-scale filtering
• Statistical mixture model
• Vessel segmentation

• Convex hull
• Graph extraction
• Junction detection
• Microvascular network
• Tubular structures

• Animals
• Datasets as Topic
• Imaging, Three-Dimensional/methods
• Microscopy, Fluorescence/methods
• Microvessels/anatomy & histology
• Models, Cardiovascular
• Sensitivity and Specificity

• R01 LM011415 NLM NIH HHS

Intensity inhomogeneity occurs in many medical images, especially in vessel images. Overcoming the difficulty due to image inhomogeneity is crucial for the segmentation of vessel image.

This paper proposes a localized hybrid level-set method for the segmentation of 3D vessel image. The proposed method integrates both local region information and boundary information for vessel segmentation, which is essential for the accurate extraction of tiny vessel structures. The local intensity information is firstly embedded into a region-based contour model, and then incorporated into the level-set formulation of the geodesic active contour model. Compared with the preset global threshold based method, the use of automatically calculated local thresholds enables the extraction of the local image information, which is essential for the segmentation of vessel images.

Experiments carried out on the segmentation of 3D vessel images demonstrate the strengths of using locally specified dynamic thresholds in our level-set method. Furthermore, both qualitative comparison and quantitative validations have been performed to evaluate the effectiveness of our proposed model.

Experimental results and validations demonstrate that our proposed model can achieve more promising segmentation results than the original hybrid method does.

• Algorithms
• Glycosaminoglycans/physiology
• Humans
• Image Enhancement
• Image Processing, Computer-Assisted
• Models, Theoretical
• Pattern Recognition, Automated

• Algorithms
• Angiography/methods
• Bayes Theorem
• Carotid Artery, Common/anatomy & histology
• Carotid Artery, Common/pathology
• Humans
• Image Processing, Computer-Assisted/methods
• Normal Distribution
• Tomography, X-Ray Computed/methods

• feature extraction
• vessel detection
• angiography
• image analysis
• segmentation

• Algorithms
• Angiography/methods
• Animals
• Artificial Intelligence
• Blood Vessels/pathology
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Imaging, Three-Dimensional/methods
• Magnetic Resonance Imaging/methods
• Pattern Recognition, Automated/methods
• Rats
• Reproducibility of Results
• Sensitivity and Specificity
• Sheep
• Tomography, X-Ray Computed/methods

• K08 HL083980-03 NHLBI NIH HHS
• R01 EB006494-03 NIBIB NIH HHS
• R01EB006494 NIBIB NIH HHS
• R01HL065662 NHLBI NIH HHS
• R01 EB006494 NIBIB NIH HHS
• K08 HL083980 NHLBI NIH HHS
• K08 HL083980-01 NHLBI NIH HHS
• R01 HL065662-04 NHLBI NIH HHS
• K08 HL083980-02 NHLBI NIH HHS
• R01 HL065662 NHLBI NIH HHS

• Algorithms
• Artificial Intelligence
• Fluorescein Angiography/methods
• Humans
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Pattern Recognition, Automated/methods
• Reproducibility of Results
• Retinal Vessels/anatomy & histology
• Retinoscopy/methods
• Sensitivity and Specificity

• Algorithms
• Blood Vessels/anatomy & histology
• Brain/blood supply
• Humans
• Image Interpretation, Computer-Assisted/methods
• Magnetic Resonance Angiography/methods
• Phantoms, Imaging

• Algorithms
• Analysis of Variance
• Artificial Intelligence
• Cerebral Arteries/anatomy & histology
• Computer Simulation
• Humans
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Magnetic Resonance Angiography/methods
• Models, Neurological
• Models, Statistical
• Pattern Recognition, Automated/methods
• Reproducibility of Results
• Sensitivity and Specificity
• Subtraction Technique

• Algorithms
• Artificial Intelligence
• Computer Simulation
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Imaging, Three-Dimensional/methods
• Information Storage and Retrieval/methods
• Models, Biological
• Pattern Recognition, Automated/methods
• Reproducibility of Results
• Sensitivity and Specificity
• Subtraction Technique

• R01-NS-037747 NINDS NIH HHS

• Anesthesia
• Animals
• Functional Residual Capacity
• Image Processing, Computer-Assisted
• Imaging, Three-Dimensional
• Lung/anatomy & histology
• Lung/diagnostic imaging
• Lung/physiology
• Lung Volume Measurements
• Male
• Mice
• Mice, Inbred C57BL
• Respiration
• Tidal Volume
• Tomography, X-Ray Computed/methods

• Algorithms
• Artificial Intelligence
• Computer Simulation
• Data Interpretation, Statistical
• Humans
• Image Enhancement/methods
• Image Interpretation, Computer-Assisted/methods
• Imaging, Three-Dimensional/methods
• Magnetic Resonance Angiography/methods
• Models, Cardiovascular
• Models, Statistical
• Pattern Recognition, Automated/methods
• Reproducibility of Results
• Sensitivity and Specificity