240 research outputs found
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Carotid artery vessel wall thickness measurement is an essential step in the
monitoring of patients with atherosclerosis. This requires accurate
segmentation of the vessel wall, i.e., the region between an artery's lumen and
outer wall, in black-blood magnetic resonance (MR) images. Commonly used
convolutional neural networks (CNNs) for semantic segmentation are suboptimal
for this task as their use does not guarantee a contiguous ring-shaped
segmentation. Instead, in this work, we cast vessel wall segmentation as a
multi-task regression problem in a polar coordinate system. For each carotid
artery in each axial image slice, we aim to simultaneously find two
non-intersecting nested contours that together delineate the vessel wall. CNNs
applied to this problem enable an inductive bias that guarantees ring-shaped
vessel walls. Moreover, we identify a problem-specific training data
augmentation technique that substantially affects segmentation performance. We
apply our method to segmentation of the internal and external carotid artery
wall, and achieve top-ranking quantitative results in a public challenge, i.e.,
a median Dice similarity coefficient of 0.813 for the vessel wall and median
Hausdorff distances of 0.552 mm and 0.776 mm for lumen and outer wall,
respectively. Moreover, we show how the method improves over a conventional
semantic segmentation approach. These results show that it is feasible to
automatically obtain anatomically plausible segmentations of the carotid vessel
wall with high accuracy.Comment: SPIE Medical Imaging 202
Maximization of Regional probabilities using Optimal Surface Graphs: Application to Carotid Artery Segmentation in MRI
__Purpose__ We present a segmentation method that maximizes regional probabilities enclosed by coupled surfaces using an Optimal Surface Graph (OSG) cut approach. This OSG cut determines the globally optimal solution given a graph constructed around an initial surface. While most methods for vessel wall segmentation only use edge information, we show that maximizing regional probabilities using an OSG improves the segmentation results. We applied this to automatically segment the vessel wall of the carotid artery in magnetic resonance images.
__Methods__ First, voxel-wise regional probability maps were obtained using a Support Vector Machine classifier trained on local image features. Then the OSG segments the regions which maximizes the regional probabilities considering smoothness and topological constraints.
__Results__ The method was evaluated on 49 carotid arteries from 30 subjects. The proposed method shows good accuracy with a Dice wall overlap of 74:1%+-4:3%, and significantly outperforms a published method based on an OSG using only surface information, the obtained segmentations using voxel-wise classification alone, and another published artery wall segmentation method based on a deformable surface model. Intra-class correlations (ICC) with manually measured lumen and wall volumes were similar to those obtained between observers. Finally, we show a good reproducibility of the method with ICC = 0:86 between the volumes measured in scans repeated within a short time interval.
__Conclusions__ In this work a new segmentation method that uses both an OSG and regional probabilities is presented. The method shows good segmentations of the carotid artery in MRI and outperformed another segmentation method that uses OSG and edge information and the voxel-wise segmentation using the probability maps
Image Analysis of the Carotid Artery: A (Semi-)Automatic Approach
In this thesis we presented several (semi-)automatic image processing techniques for analyzing the carotid artery wall and carotid artery plaque in MRI and Ultrasound. The presented methods include image segmentation, registration, centerline extraction, and quantification
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