U-RSNet: An unsupervised probabilistic model for joint registration and segmentation

Medical image segmentation and registration have vital roles in computer-assisted diagnosis procedures, challenging tasks suffering from various limitations and artifacts inside images. Recently, deep learning techniques accomplish these two tasks and achieve outstanding performances. However, most deep learning-based methods overlook the potential correlation between each other. In this paper, an unsupervised probabilistic model named U-RSNet is proposed to realize concurrent medical image registration and segmentation in one framework. Specifically, the unsupervised segmentation branch is derived from Bayesian inference. The prior warped atlas for segmentation can be obtained by deforming a known probabilistic atlas by the corresponding invertible deformation field with a well-behaved diffeomorphic guarantee, which can perfectly integrate these two tasks to form a complete intelligent prediction system. In this case, the segmentation performance could be largely improved based on the warped probabilistic atlas obtained from the registration branch. Experiments on human brain 3D magnetic resonance images have demonstrated the effectiveness of our approach. We trained and validated U-RSNet with 1000 images and tested its performances on four public datasets. We showed our method successfully realized concurrent segmentation and registration and yielded better segmentation results than a separately trained network. (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

The study proposes an unsupervised probabilistic model called U-RSNet for concurrent medical image registration and segmentation. By integrating deep learning techniques with Bayesian inference, the segmentation performance has been successfully improved.