Learning‐based automated segmentation of the carotid artery vessel wall in dual‐sequence MRI using subdivision surface fitting

Gao, Shan; Klooster, R. van ˈt; Kitslaar, Pieter H.; Coolen, Bram F.; Berg, Alexandra M. van den; Smits, Loek P.; Shahzad, Rahil; Shamonin, Denis P; Koning, Patrick J.H. de; Nederveen, Aart J.; Geest, Rob J. van der

doi:10.1002/mp.12476

Cited by 18 publications

(17 citation statements)

References 38 publications

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“…However, due to the complex signal characteristics found near the vessel walls, the slice-by-slice analysis of the VW-MRI images with moderate interreader reproducibility is cumbersome for the plaque burden assessment. 16 Some automated or semi-automated segmentation methods have been proposed for the carotid artery, and the lumen and the outer wall boundaries are typically segmented in a two-dimensional (2D) slice-by-slice mode [17][18][19][20][21][22] or three-dimensional (3D) mode, [23][24][25][26][27] where the lumen and the outer wall boundaries are depicted as the contours to calculate the plaque burden metrics for the diagnosis of atherosclerosis. The 2D parametric deformable model-based methods (e.g., the snake contour 17 and the discrete dynamic contour 18 ) segment the carotid lumen boundary and the outer wall boundary on each transverse slice, but they require considerable manual interaction (initial contours).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the automatic segmentation method based on the 3D deformable vessel models was proposed for the common carotid artery (CCA) by combing 3D time‐of‐flight (TOF) MR angiography (MRA) and 2D vessel wall images, while the 3D coupled optimal surface graph‐cut method was presented for the carotid artery wall segmentation by using the proton density‐weighted (PDW) black‐blood (BB) MRI (BB‐MRI), phase contrast MRI, PDW echo planar imaging MRI and/or BB‐MRI images. Additionally the learning‐based method was developed for the segmentation of carotid artery wall including the carotid bifurcation from dual BBMRI and TOF‐MRA images by combing the 3D fitting of a subdivision surface‐based hierarchical‐tree model with the K‐nearest neighbor based boundary classification. However, these 3D techniques require the acquisition of other modalities such as the MR angiography to aid in the segmentation.…”

Section: Introductionmentioning

confidence: 99%

“…Additionally the learning‐based method was developed for the segmentation of carotid artery wall including the carotid bifurcation from dual BBMRI and TOF‐MRA images by combing the 3D fitting of a subdivision surface‐based hierarchical‐tree model with the K‐nearest neighbor based boundary classification. However, these 3D techniques require the acquisition of other modalities such as the MR angiography to aid in the segmentation. Unfortunately, most of these methods are usually performed only by the quantitative analysis of carotid atherosclerosis plaque burden such as the vessel wall thickness and the normalized wall index to complete the carotid atherosclerosis studies.…”

Section: Introductionmentioning

confidence: 99%

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Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black‐blood vessel wall MRI

Xin

Yang

et al. 2019

Medical Physics

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Purpose Early detection of carotid atherosclerosis on the vessel wall (VW) magnetic resonance imaging (MRI) (VW‐MRI) images can prevent the progression of cardiovascular disease. However, the manual inspection process of the VW‐MRI images is cumbersome and has low reproducibility. Therefore in this paper, by using the convolutional neural networks (CNNs), we develop a deep morphology aided diagnosis (DeepMAD) network for automated segmentation of the VW of carotid artery and for automated diagnosis of the carotid atherosclerosis with the black‐blood (BB) VW‐MRI (i.e., the T1‐weighted MRI) in a slice‐by‐slice manner. Methods The proposed DeepMAD network consists of a segmentation subnetwork and a diagnosis subnetwork for performing the segmentation and diagnosis tasks on the BB‐VW‐MRI images, where the manual labeled lumen area, the manual labeled outer wall area and the manual labeled lesion Types based on the modified American Heart Association (AHA) criteria are used as the ground‐truth. Specifically, a deep U‐shape CNN with a weighted fusion layer is designed as the segmentation subnetwork, where the lumen area and the outer wall area can be simultaneously segmented under the supervision of the triple Dice loss to provide the vessel wall map as morphological information. Then, the image stream from the BB‐VWMRI image and the morphology stream from the obtained vessel wall map are extracted from two deep CNNs and combined to obtain the diagnosis results of atherosclerosis in the diagnosis subnetwork. In addition, the triple input set is formed by three carotid regions of interest (ROIs) from three consecutive slices of the MRI sequence and input to the DeepMAD network, where the first and last slices used as additional adjacent slices to provide 2.5D spatial information along the carotid artery centerline for the intermediate slice, which is the target slice for segmentation and diagnosis in the study. Results Compared to other existing methods, the DeepMAD network can achieve promising segmentation performances (0.9594 Dice for the lumen and 0.9657 Dice for the outer wall) and better diagnosis Accuracy of the carotid atherosclerosis (0.9503 AUC and 0.8916 Accuracy) in the test dataset (including invisible subjects) from same source as the training dataset. In addition, the trained DeepMAD model can be successfully transferred to another test dataset for segmentation and diagnosis tasks with remarkable performance (0.9475 Dice for the lumen and 0.9542 Dice for the outer wall, 0. 9227 AUC and 0.8679 Accuracy for diagnosis). Conclusions Even without the intervention of reviewers required for previous works, the proposed DeepMAD network automatically segments the lumen and the outer wall together and diagnoses the carotid atherosclerosis with high performances. The DeepMAD network can be used in clinical trials to help radiologists get rid of tedious reading tasks, such as screening review to separate the normal carotid from the atherosclerotic arteries and outlining the vessel wall contours.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black‐blood vessel wall MRI

Xin

Yang

et al. 2019

Medical Physics

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“…While arteries with 0–49% stenosis were detected with 100% agreement, arteries with 50–69% and 70–99% stenosis showed agreement of 57% and 77%, respectively. In a recent first of its kind attempt, using a hierarchical-tree model along with the application of k -NN supervised classifier to detect the lumen boundary, in both normal (15) and atherosclerotic (20) subjects, a similarity index (Dice overlap), between the software and the manual delineation, of 0.87 was achieved [42].…”

Section: Introductionmentioning

confidence: 99%

Imaging modalities to diagnose carotid artery stenosis: progress and prospect

2019

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In the past few decades, imaging has been developed to a high level of sophistication. Improvements from one-dimension (1D) to 2D images, and from 2D images to 3D models, have revolutionized the field of imaging. This not only helps in diagnosing various critical and fatal diseases in the early stages but also contributes to making informed clinical decisions on the follow-up treatment profile. Carotid artery stenosis (CAS) may potentially cause debilitating stroke, and its accurate early detection is therefore important. In this paper, the technical development of various CAS diagnosis imaging modalities and its impact on the clinical efficacy is thoroughly reviewed. These imaging modalities include duplex ultrasound (DUS), computed tomography angiography (CTA) and magnetic resonance angiography (MRA). For each of the imaging modalities considered, imaging methodology (principle), critical imaging parameters, and the extent of imaging the vulnerable plaque are discussed. DUS is usually the initial recommended CAS diagnostic examination. However, for the therapeutic intervention, either MRA or CTA is recommended for confirmation, and for added information on intracranial cerebral circulation and aortic arch condition for procedural planning. Over the past few decades, the focus of CAS diagnosis has also shifted from pure stenosis quantification to plaque characterization. This has led to further advancement in the existing imaging tools and development of other potential imaging tools like Optical coherence tomography (OCT), photoacoustic tomography (PAT), and infrared (IR) thermography.

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“…In general, all of these methods are semi‐automatic with a need of user's interactions. A few fully automatic approaches [25–27] have been proposed to segment the lumen region only. Both of OTSU thresholding with directional gradient [25] and local adaptive thresholding approaches [26] had been implemented to segment LB automatically.…”

Section: Introductionmentioning

confidence: 99%

Automatic segmentation of MR depicted carotid arterial boundary based on local priors and constrained global optimisation

Zhang

Qin

et al. 2019

IET Image Processing

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Learning‐based automated segmentation of the carotid artery vessel wall in dual‐sequence MRI using subdivision surface fitting

Cited by 18 publications

References 38 publications

Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black‐blood vessel wall MRI

Deep morphology aided diagnosis network for segmentation of carotid artery vessel wall and diagnosis of carotid atherosclerosis on black‐blood vessel wall MRI

Imaging modalities to diagnose carotid artery stenosis: progress and prospect

Automatic segmentation of MR depicted carotid arterial boundary based on local priors and constrained global optimisation

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