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Multi-modality is widely used in medical imaging, because it can providemultiinformation about a target (tumor, organ or tissue). Segmentation usingmultimodality consists of fusing multi-information to improve the segmentation.Recently, deep learning-based approaches have presented the state-of-the-artperformance in image classification, segmentation, object detection andtracking tasks. Due to their self-learning and generalization ability overlarge amounts of data, deep learning recently has also gained great interest inmulti-modal medical image segmentation. In this paper, we give an overview ofdeep learning-based approaches for multi-modal medical image segmentation task.Firstly, we introduce the general principle of deep learning and multi-modalmedical image segmentation. Secondly, we present different deep learningnetwork architectures, then analyze their fusion strategies and compare theirresults. The earlier fusion is commonly used, since it's simple and it focuseson the subsequent segmentation network architecture. However, the later fusiongives more attention on fusion strategy to learn the complex relationshipbetween different modalities. In general, compared to the earlier fusion, thelater fusion can give more accurate result if the fusion method is effectiveenough. We also discuss some common problems in medical image segmentation.Finally, we summarize and provide some perspectives on the future research.

A review: Deep learning for medical image segmentation using multi-modality fusion