Carotid atherosclerotic plaque segmentation in multi-weighted MRI using a two-stage neural network: advantages of training with high-resolution imaging and histology

Abstract

IntroductionA reliable and automated method to segment and classify carotid artery atherosclerotic plaque components is needed to efficiently analyze multi-weighted magnetic resonance (MR) images to allow their integration into patient risk assessment for ischemic stroke. Certain plaque components such as lipid-rich necrotic core (LRNC) with hemorrhage suggest a greater likelihood of plaque rupture and stroke event. Assessment for presence and extent of LRNC could assist in directing treatment with impact upon patient outcomes.MethodsTo address the need to accurately determine the presence and extent of plaque components on carotid plaque MRI, we proposed a two-staged deep-learning-based approach that consists of a convolutional neural network (CNN), followed by a Bayesian neural network (BNN). The rationale for the two-stage network approach is to account for the class imbalance of vessel wall and background by providing an attention mask to the BNN. A unique feature of the network training was to use ground truth defined by both high-resolution ex vivo MRI data and histopathology. More specifically, standard resolution 1.5 T in vivo MR image sets with corresponding high resolution 3.0 T ex vivo MR image sets and histopathology image sets were used to define ground-truth segmentations. Of these, data from 7 patients was used for training and from the remaining two was used for testing the proposed method. Next, to evaluate the generalizability of the method, we tested the method with an additional standard resolution 3.0 T in vivo data set of 23 patients obtained from a different scanner.ResultsOur results show that the proposed method yielded accurate segmentation of carotid atherosclerotic plaque and outperforms not only manual segmentation by trained readers, who did not have access to the ex vivo or histopathology data, but also three state-of-the-art deep-learning-based segmentation methods. Further, the proposed approach outperformed a strategy where the ground truth was generated without access to the high resolution ex vivo MRI and histopathology. The accurate performance of this method was also observed in the additional 23-patient dataset from a different scanner.ConclusionIn conclusion, the proposed method provides a mechanism to perform accurate segmentation of the carotid atherosclerotic plaque in multi-weighted MRI. Further, our study shows the advantages of using high-resolution imaging and histology to define ground truth for training deep-learning-based segmentation methods.

CACSNet for automatic robust classification and segmentation of carotid artery calcification on panoramic radiographs using a cascaded deep learning network

“…CACSNet with EfficientNet-B4 backbone achieved a JI of 0.595, DSC of 0.722, precision of 0.749, and recall of 0.756. Several investigations of CAC segmentation in images other than PRs using deep learning models have been conducted 22 – 24 , 55 – 57 . The deep learning models achieved a DSC of 0.795 in CT angiographic images 23 , a DSC of 0.9381 in ultrasound images 24 , and a DSC of 0.78, precision of 0.76, and recall of 0.8 in MR images 22 .…”

“…Several investigations of CAC segmentation in images other than PRs using deep learning models have been conducted 22 – 24 , 55 – 57 . The deep learning models achieved a DSC of 0.795 in CT angiographic images 23 , a DSC of 0.9381 in ultrasound images 24 , and a DSC of 0.78, precision of 0.76, and recall of 0.8 in MR images 22 . Therefore, compared with previous studies performed on other modalities, CACSNet demonstrated comparable segmentation performance for CACs on PRs.…”

“…To date, no automatic segmentation studies of CAC lesions in PRs using deep learning have been published, but there are many studies of segmentation of CACs on images taken using other imaging modalities 22 – 24 , 55 – 57 . This is likely due to the limited diagnostic accuracy of conventional PRs in assessing CAC, coupled with the difficulty in accurately labeling ground truth for CAC lesions on PRs.…”

“…There have been several studies on the automatic segmentation of CAC from MR, CT, and ultrasound images using deep learning. A two-stage deep learning-based method was proposed for segmentation of carotid atherosclerotic plaques in multi-weighted MR images 22 . A deep learning-based method was developed to segment CAC in CT angiography 23 .…”