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.

本研究提出了一种新的框架，Modality-Agnostic learning through Multi-modality Self-dist-illation (MAG-MS)，用于研究输入多模式的影响，以增强个体模式的表示学习，从而提高医学图像分割的效率和准确性，并提供了在选择输入模式方面有价值的见解和指导。

基于多模态自蒸馏的医学图像分割模态不可知学习

Modality-Agnostic Learning for Medical Image Segmentation Using  Multi-modality Self-distillation

In medical imaging, image synthesis is the estimation process of one image
(sequence, modality) from another image (sequence, modality). Since images with
different modalities provide diverse biomarkers and capture various features,
multi-modality imaging is crucial in medicine. While multi-screening is
expensive, costly, and time-consuming to report by radiologists, image
synthesis methods are capable of artificially generating missing modalities.
Deep learning models can automatically capture and extract the high dimensional
features. Especially, generative adversarial network (GAN) as one of the most
popular generative-based deep learning methods, uses convolutional networks as
generators, and estimated images are discriminated as true or false based on a
discriminator network. This review provides brain image synthesis via GANs. We
summarized the recent developments of GANs for cross-modality brain image
synthesis including CT to PET, CT to MRI, MRI to PET, and vice versa.

本文主要介绍了使用生成对抗网络（GAN）进行医学图像合成的研究。作者总结了最近应用 GAN 进行交叉模态大脑图像合成的进展，包括 CT 合成 PET、CT 合成 MRI、MRI 合成 PET 以及相反的方向。