面向医学图像分割的自监督对齐学习
Self-Supervised Alignment Learning for Medical Image Segmentation
AI总结：
针对现有自监督医学图像分割预训练未充分利用3D图像2D切片空间对应性的问题，提出含局部对齐损失和全局位置损失的自监督对齐学习框架，在标注有限的CT、MRI数据集上性能与现有方法相当。
摘要：
近年来，自监督学习（SSL）方法已被用于预训练二维和三维医学图像的分割模型，这类方法大多基于重建、对比学习和一致性正则化。然而，三维医学图像中二维切片的空间对应关系尚未得到充分利用。本文提出一种新型自监督对齐学习框架，用于预训练医学图像分割神经网络，该框架包含全新的局部对齐损失和全局位置损失。我们观察到，在同一次三维扫描中，两个相邻的二维切片通常包含相似的解剖结构，因此提出局部对齐损失，使匹配结构的像素级特征彼此接近。实验结果表明，在标注有限的设置下，所提出的对齐学习方法在CT和MRI数据集上的性能与现有自监督预训练方法相当。
英文摘要：
Recently, self-supervised learning (SSL) methods have been used in pre-training the segmentation models for 2D and 3D medical images. Most of these methods are based on reconstruction, contrastive learning and consistency regularization. However, the spatial correspondence of 2D slices from a 3D medical image has not been fully exploited. In this paper, we propose a novel self-supervised alignment learning framework to pre-train the neural network for medical image segmentation. The proposed framework consists of a new local alignment loss and a global positional loss. We observe that in the same 3D scan, two close 2D slices usually contain similar anatomic structures. Thus, the local alignment loss is proposed to make the pixel-level features of matched structures close to each other. Experimental results show that the proposed alignment learning is competitive with existing self-supervised pre-training approaches on CT and MRI datasets, under the setting of limited annotations.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
