论文标题
通过几何感知一致性训练进行半监督的医学图像分割
Semi-supervised Medical Image Segmentation via Geometry-aware Consistency Training
论文作者
论文摘要
监督的深度学习方法的医学图像细分方法通常受到标记数据的稀缺性的限制。作为一个有希望的研究方向，半监督的学习通过利用未标记的数据信息来帮助学习过程来解决这一困境。在本文中，为医学图像分割提出了一种新颖的几何学吸引半监督学习框架，这是一种基于一致性的方法。考虑到难以段的区域主要位于对象边界周围，我们引入了一项辅助预测任务，以学习全局几何信息。基于几何约束，通过指数加权的策略来强调模型训练，以更好地利用标记和未标记的数据来强调边界区域。此外，双视网网络旨在从不同的角度执行细分并减少预测不确定性。该方法在公共场所进行评估，左心房基准数据集，并以10％标记的图像的骰子将完全监督的方法提高了8.7％，而标记为20％的图像的图像为4.3％。同时，我们的框架优于六种最先进的半监督分割方法。
The performance of supervised deep learning methods for medical image segmentation is often limited by the scarcity of labeled data. As a promising research direction, semi-supervised learning addresses this dilemma by leveraging unlabeled data information to assist the learning process. In this paper, a novel geometry-aware semi-supervised learning framework is proposed for medical image segmentation, which is a consistency-based method. Considering that the hard-to-segment regions are mainly located around the object boundary, we introduce an auxiliary prediction task to learn the global geometric information. Based on the geometric constraint, the ambiguous boundary regions are emphasized through an exponentially weighted strategy for the model training to better exploit both labeled and unlabeled data. In addition, a dual-view network is designed to perform segmentation from different perspectives and reduce the prediction uncertainty. The proposed method is evaluated on the public left atrium benchmark dataset and improves fully supervised method by 8.7% in Dice with 10% labeled images, while 4.3% with 20% labeled images. Meanwhile, our framework outperforms six state-of-the-art semi-supervised segmentation methods.
