Medical image segmentation of tumors and organs at risk is a time-consuming yet critical process in the clinic that utilizes multi-modality imaging (e.g, different acquisitions, data types, and sequences) to increase segmentation precision. In this paper, we propose a novel framework, Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS), to investigate the impact of input modalities on medical image segmentation. MAG-MS distills knowledge from the fusion of multiple modalities and applies it to enhance representation learning for individual modalities. Thus, it provides a versatile and efficient approach to handle limited modalities during testing. Our extensive experiments on benchmark datasets demonstrate the high efficiency of MAG-MS and its superior segmentation performance than current state-of-the-art methods. Furthermore, using MAG-MS, we provide valuable insight and guidance on selecting input modalities for medical image segmentation tasks.

翻译：医学图像中肿瘤及危及器官的分割是临床中一项耗时但至关重要的流程，该流程利用多模态成像（例如不同采集方式、数据类型和序列）以提高分割精度。本文提出一种新颖框架——基于多模态自蒸馏的模态无关学习（MAG-MS），旨在研究输入模态对医学图像分割的影响。MAG-MS通过从多模态融合中蒸馏知识，并将其应用于增强各模态的表示学习，从而提供一种在测试阶段处理有限模态的灵活高效方案。我们在基准数据集上的大量实验表明，MAG-MS具有高效性，其分割性能优于当前最先进方法。此外，利用MAG-MS，我们为医学图像分割任务中输入模态的选择提供了有价值的见解与指导。