Automatic segmentation of organs‐at‐risks of nasopharynx cancer and lung cancer by cross‐layer attention fusion network with TELD‐Loss. Issue 11 (18th October 2021)

• Record Type: Journal Article
• Authors: Liu, Zuhao; Sun, Chao; Wang, Huan; Li, Zhiqi; Gao, Yibo; Lei, Wenhui; Zhang, Shichuan; Wang, Guotai; Zhang, Shaoting
• Is Part Of: Medical physics. Volume 48:Issue 11(2021)
• Page Start: 6987
• Page End: 7002
• Publication Date: 2021-10-18
• Subjects: automatic segmentation -- deep learning -- organs at risk -- radiotherapy planning; Medical physics -- Periodicals; Medical physics; Geneeskunde; Natuurkunde; Toepassingen; Biophysics; Periodicals; Periodicals; Electronic journals; 610.153
• DOI: 10.1002/mp.15260
• Languages: English
• ISSNs: 0094-2405

Abstract

Abstract: Purpose : Radiotherapy is one of the main treatments of nasopharyngeal cancer (NPC) and lung cancer. Accurate segmentation of organs at risks (OARs) in CT images is a key step in radiotherapy planning for NPC and lung cancer. However, the segmentation of OARs is influenced by the highly imbalanced size of organs, which often results in very poor segmentation results for small and difficult‐to‐segment organs. In addition, the complex morphological changes and fuzzy boundaries of OARs also pose great challenges to the segmentation task. In this paper, we propose a cross‐layer attention fusion network (CLAF‐CNN) to solve the problem of accurately segmenting OARs. Methods : In CLAF‐CNN, we integrate the spatial attention maps of the adjacent spatial attention modules to make the segmentation targets more accurately focused, so that the network can capture more target‐related features. In this way, the spatial attention modules in the network can be learned and optimized together. In addition, we introduce a new Top‐K exponential logarithmic Dice loss (TELD‐Loss) to solve the imbalance problem in OAR segmentation. The TELD‐Loss further introduces a Top‐K optimization mechanism based on Dice loss and exponential logarithmic loss, which makes the network pay more attention to small organs and difficult‐to‐segment organs, so as to enhance the overall performance of the segmentation model. Results : We validated our framework on the OAR segmentation datasets of the head and neck and lung CT images in the StructSeg 2019 challenge. Experiments show that the CLAF‐CNN outperforms the state‐of‐the‐art attention‐based segmentation methods in the OAR segmentation task with average Dice coefficient of 79.65% for head and neck OARs and 88.39% for lung OARs. Conclusions : This work provides a new network named CLAF‐CNN which contains cross‐layer spatial attention map fusion architecture and TELD‐Loss for OAR segmentation. Results demonstrated that the proposed method could obtain accurate segmentation results for OARs, which has a potential of improving the efficiency of radiotherapy planning for nasopharynx cancer and lung cancer.