RSegNet: A Joint Learning Framework for Deformable Registration and Segmentation

Abstract

Medical image segmentation and registration are two tasks to analyze the anatomical structures in clinical research. Still, deep-learning solutions utilizing the connections between segmentation and registration remain underdiscovered. This article designs a joint learning framework named RSegNet that can realize concurrent deformable registration and segmentation by minimizing an integrated loss function, including three parts: diffeomorphic registration loss, segmentation similarity loss, and dual-consistency supervision loss. The probabilistic diffeomorphic registration branch could benefit from the auxiliary segmentations available from the segmentation branch to achieve anatomical consistency and better deformation regularity by dual-consistency supervision. Simultaneously, the segmentation performance could also be improved by data augmentation based on the registration with well-behaved diffeomorphic guarantees. Experiments on the human brain 3-D magnetic resonance images have been implemented to demonstrate the effectiveness of our approach. We trained and validated RSegNet with 1000 images and tested its performances on four public datasets, which shows that our method successfully yields concurrent improvements of both segmentation and registration compared with separately trained networks. Specifically, our method can increase the accuracy of segmentation and registration by 7.0% and 1.4%, respectively, in terms of Dice scores. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Registration and segmentation of medical images are two significant tasks in medical research and clinical application. However, most existing approaches consider these two tasks independently while neglecting the potential association between them. Therefore, we suggest a new approach that combines these two tasks into one joint deep learning framework, boosting registration, and segmentation performance by introducing dual-consistency supervision. Besides, our framework could generate outputs within 1 s by taking an affinely aligned medical image pair as input, which is suitable for time-critical requirements in a clinic. We tested it on four public datasets and achieved state-of-the-art performance to demonstrate the proposed method’s feasibility and robustness. Furthermore, our proposed RSegNet is a general learning framework suitable for various image modalities and anatomical structures. Hence, we expect our framework to serve as a practical clinical tool to speed up medical image analysis procedures and improve diagnostic accuracy.