DSANet: Dual-path segmentation-guided attention network for radiotherapy dose prediction from CT images only

Abstract

In radiation therapy, an ideal treatment plan always takes the treatment planner a couple of days to tweak repeatedly, owing to the complex dose distribution which requires the prescribed dose in the tumor area but significantly reduced radiation dose in the surrounding normal tissues. To facilitate this process, many deep learning-based studies have been devoted to the automatical prediction of dose distribution. However, besides the computed tomography (CT) image, these methods usually require extra segmentation masks such as tumor target and organs at risk (OARs) as prior knowledge, and acquiring such annotations are quite time-consuming. In this paper, we propose a Dual-path Segmentation-guided Attention Network (DSANet) to directly predict the dose distribution map solely from the CT images. Specifically, considering the high correlation between the tumor anatomical structure and the dose distribution, we develop a dual-path architecture which consists of 1) a primary dose prediction path aiming at generating accurate dose distribution maps, and 2) an auxiliary segmentation path applied to provide attention maps with anatomical information for the primary dose prediction task. Moreover, we design an attention map-guided feature decoupling (AMFD) module to enforce the primary network to focus not only on the high-dose distribution in the tumor area but also on the low-dose distribution in the surrounding regions. Experiments on two in-house datasets demonstrate the effectiveness and generalization of our method.