Carotid vessel wall segmentation is a crucial yet challenging task in the computer-aided diagnosis of atherosclerosis. Although numerous deep learning models have achieved remarkable success in many medical image segmentation tasks, accurate segmentation of carotid vessel wall on magnetic resonance (MR) images remains challenging, due to limited annotations and heterogeneous arteries. In this paper, we propose a semi-supervised label propagation framework to segment lumen, normal vessel walls, and atherosclerotic vessel wall on 3D MR images. By interpolating the provided annotations, we get 3D continuous labels for training 3D segmentation model. With the trained model, we generate pseudo labels for unlabeled slices to incorporate them for model training. Then we use the whole MR scans and the propagated labels to re-train the segmentation model and improve its robustness. We evaluated the label propagation framework on the CarOtid vessel wall SegMentation and atherosclerOsis diagnosiS (COSMOS) Challenge dataset and achieved a QuanM score of 83.41\% on the testing dataset, which got the 1-st place on the online evaluation leaderboard. The results demonstrate the effectiveness of the proposed framework.
翻译:虽然许多深层学习模型在许多医学图像分割任务中取得了显著的成功,但磁共振(MR)图像上的碳化容器壁的准确分解仍然具有挑战性,因为说明有限,动动脉不一。在本文中,我们提议对3D MR 图像上的润滑部分、正常容器壁和静默感应容器壁进行半监督标签传播框架。通过对所提供的说明进行相互调试,我们获得了用于培训3D分解模型的3D连续标签。在经过培训的模型中,我们为未贴标签的切片制作了假标签,以将其纳入模型培训。然后,我们利用整个光共振扫描和已传播标签对分解模型进行再培训并提高其稳定性。我们评估了CarOtid 船舶壁SegMentation的标签传播框架和therosclerOsis DiagnosiS(COSOS) 的标签传播框架。我们为培训了3D分解剖图,并实现了8341* 的定量M评分数。我们用测试框架上的拟议测试结果展示了在线结果。