Automatic segmentation of the clinical target volume and organs at risk for rectal cancer radiotherapy using structure-contextual representations based on 3D high-resolution network. (March 2022) - Yang, Yiwei
Highlights: A novel segmentation framework for rectal cancer radiotherapy is presented. We are the first to extend the advanced 2D segmentation model HRNet to 3D paradigm. An effective Structure-Contextual Representations (SCR) module is presented. SCR is a plug-and-play module, which improves the accuracy of the model. Abstract: Radiotherapy is a cancer treatment that uses high doses of radiation to kill cancer cells. Segmentation of the clinical target volumes (CTVs) and organs at risk (OARs) is an essential step in rectal cancer radiotherapy treatment planning. However, the manual segmentation of CTVs and OARs is labor-intensive and prone to intra- and inter-rater variations. The challenge of this task lies in large shape variations and low-contrast structure boundary for some structures like intestine. The contextual information is very important to handle this challenge. Recently, deep learning has greatly improved the state-of-the-art results in automatic segmentation of anatomical structures including OARs and CTVs. However, existing approaches barely take advantages of relations among anatomical structures as contextual information, which limits the segmentation accuracy. In this study, we propose a novel structure-contextual representations approach based on 3D high-resolution network (3D HRNet-SCR) for segmentation of OARs and CTV of rectal cancer. First, we design a structure-contextual representation module (SCR) which could compute the representation for each structure of interest by aggregating the representations of voxels inside the structure and enhance the feature representation of each voxel with learned structure-contextual representations. Second, we extend the powerful 2D HRNet for semantic segmentation to 3D paradigm to better capture the contextual information across slices of volumetric data. Our propose approach integrates the proposed SCR module on top of the 3D HRNet to form a high-performance segmentation framework. Finally, we collect a large-scale rectal cancer dataset of 536 CT scans (total 64320 slices) for evaluation. Our proposed framework is extensively tested on this self-collected dataset, showing superiority compared with state-of-the-art OARs and CTV segmentation methods for rectal cancer treatment.