论文标题
形状感知的半监督3D语义分割的医学图像
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images
论文作者
论文摘要
由于获取像素图像注释的挑战，半监督的学习吸引了医疗图像细分的关注，这是构建高性能深度学习方法的关键步骤。大多数现有的半监督分割方法要么倾向于忽略对象段中的几何约束，从而导致对象覆盖不完整，或者在此之前施加了强大的形状，这需要额外的对齐方式。在这项工作中，我们提出了一种新型的Shapeaware半监督分割策略，以利用丰富的未标记数据，并对分割输出实施几何形状约束。为了实现这一目标，我们开发了一个多任务深网，该网络共同预测对象表面的语义分割和签名距离图（SDM）。在培训期间，我们在标记和未标记数据的预测SDM之间引入了对抗性损失，以便我们的网络能够更有效地捕获形状感知功能。对心房分割挑战数据集的实验表明，我们的方法的表现优于当前最新方法，并改善了形状估计，从而验证了其功效。代码可在https://github.com/kleinzcy/sassnet上找到。
Semi-supervised learning has attracted much attention in medical image segmentation due to challenges in acquiring pixel-wise image annotations, which is a crucial step for building high-performance deep learning methods. Most existing semi-supervised segmentation approaches either tend to neglect geometric constraint in object segments, leading to incomplete object coverage, or impose strong shape prior that requires extra alignment. In this work, we propose a novel shapeaware semi-supervised segmentation strategy to leverage abundant unlabeled data and to enforce a geometric shape constraint on the segmentation output. To achieve this, we develop a multi-task deep network that jointly predicts semantic segmentation and signed distance map(SDM) of object surfaces. During training, we introduce an adversarial loss between the predicted SDMs of labeled and unlabeled data so that our network is able to capture shape-aware features more effectively. Experiments on the Atrial Segmentation Challenge dataset show that our method outperforms current state-of-the-art approaches with improved shape estimation, which validates its efficacy. Code is available at https://github.com/kleinzcy/SASSnet.
