Unsupervised MMRegNet based on Spatially Encoded Gradient Information

• URL: http://arxiv.org/abs/2105.07392v1
• Date: Sun, 16 May 2021 09:47:42 GMT
• Title: Unsupervised MMRegNet based on Spatially Encoded Gradient Information
• Authors: Wangbin Ding, Lei Li, Xiahai Zhuang, Liqin Huang
• Abstract summary: Multi-modality medical images can provide relevant and complementary anatomical information for a target (organ, tumor or tissue) It is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training. In this work, we propose a multi-modality registration network (MMRegNet), which can jointly register multiple images with different modalities to a target image.
• Score: 16.355832135847276
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-modality medical images can provide relevant and complementary anatomical information for a target (organ, tumor or tissue). Registering the multi-modality images to a common space can fuse these comprehensive information, and bring convenience for clinical application. Recently, neural networks have been widely investigated to boost registration methods. However, it is still challenging to develop a multi-modality registration network due to the lack of robust criteria for network training. Besides, most existing registration networks mainly focus on pairwise registration, and can hardly be applicable for multiple image scenarios. In this work, we propose a multi-modality registration network (MMRegNet), which can jointly register multiple images with different modalities to a target image. Meanwhile, we present spatially encoded gradient information to train the MMRegNet in an unsupervised manner. The proposed network was evaluated on two datasets, i.e, MM-WHS 2017 and CHAOS 2019. The results show that the proposed network can achieve promising performance for cardiac left ventricle and liver registration tasks. Source code is released publicly on github.

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