Radiation therapy is a crucial part of cancer treatment, with nearly half of all cancer patients receiving it at some point during their illness. It usually takes a radiation oncologist several hours to delineate the targets and organs-at-risk (OARs) for a radiotherapy treatment planning (RTP). Worse, the inevitable registration errors between computed tomography (CT) images and magnetic resonance imaging (MR) images increase the uncertainty of delineation. Although some deep-learning based segmentation and synthesis methods have been proposed to solve the above-mentioned difficulties respectively, they ignore the potential relationship between the two tasks. The technology proposed in this paper takes the synergy of synthesis and segmentation into account and achieves superior performance in both tasks. Our method can automatically realize MR-CT synthesis and segmentation of targets and OARs only based on MR images in half a minute, which will simplify the workflow of RTP and improve ...

The synthesis of computed tomography (CT) images from magnetic resonance imaging (MR) images and segmentation of target and organs-at-risk (OARs) are two important tasks in MR-only radiotherapy treatment planning (RTP). Some methods have been proposed to utilize the paired MR and CT images for MR-CT synthesis or target and OARs segmentation. However, these methods usually handle synthesis and segmentation as two separate tasks, and ignore the inevitable registration errors in paired images after standard registration. In this article, we propose a cross-task feedback fusion generative adversarial network (CTFF-GAN) for joint MR-CT synthesis and segmentation of target and OARs to enhance each task’s performance. Specifically, we propose a cross-task feedback fusion (CTFF) module to feedback the semantic information from the segmentation task to the synthesis task for the anatomical structure correction in synthetic CT images. Besides, we use CT images synthesized from MR images for mult...