论文标题
3D颈动脉壁分割和动脉粥样硬化诊断的标签传播
Label Propagation for 3D Carotid Vessel Wall Segmentation and Atherosclerosis Diagnosis
论文作者
论文摘要
颈动脉容器壁分割是动脉粥样硬化的计算机辅助诊断中的一项至关重要但具有挑战性的任务。尽管许多深度学习模型在许多医学图像分割任务中取得了巨大的成功，但由于注释有限和异构动脉，对磁共振（MR）图像上颈动脉壁（MR）图像的准确分割仍然具有挑战性。在本文中，我们在3D MR图像上提出了一个半监督标签的传播框架，以分段腔，正常容器壁和动脉粥样硬化血管壁。通过插值提供的注释，我们获得了3D连续标签，用于训练3D分割模型。借助训练有素的模型，我们生成了未标记切片的伪标签，以将其整合到模型训练中。然后，我们使用整个MR扫描和传播标签来重新培养分割模型并改善其鲁棒性。我们评估了颈动脉容器墙分割和动脉粥样硬化诊断（COSMOS）挑战数据集上的标签传播框架，并在测试数据集中获得了83.41 \％的标签，在在线评估排行榜上获得了1-ST的排名。结果证明了所提出的框架的有效性。
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.
