Deform-GAN:An Unsupervised Learning Model for Deformable Registration

• URL: http://arxiv.org/abs/2002.11430v1
• Date: Wed, 26 Feb 2020 12:20:46 GMT
• Title: Deform-GAN:An Unsupervised Learning Model for Deformable Registration
• Authors: Xiaoyue Zhang, Weijian Jian, Yu Chen, Shihting Yang
• Abstract summary: In this paper, a non-rigid registration method is proposed for 3D medical images leveraging unsupervised learning. The proposed gradient loss is robust across sequences and modals for large deformation. Neither ground-truth nor manual labeling is required during training.
• Score: 4.030402376540977
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deformable registration is one of the most challenging task in the field of medical image analysis, especially for the alignment between different sequences and modalities. In this paper, a non-rigid registration method is proposed for 3D medical images leveraging unsupervised learning. To the best of our knowledge, this is the first attempt to introduce gradient loss into deep-learning-based registration. The proposed gradient loss is robust across sequences and modals for large deformation. Besides, adversarial learning approach is used to transfer multi-modal similarity to mono-modal similarity and improve the precision. Neither ground-truth nor manual labeling is required during training. We evaluated our network on a 3D brain registration task comprehensively. The experiments demonstrate that the proposed method can cope with the data which has non-functional intensity relations, noise and blur. Our approach outperforms other methods especially in accuracy and speed.

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