Cascaded‐TOARNet: A cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation

Abstract Background Accurate and automated segmentation of thoracic organs‐at‐risk (OARs) is critical for radiotherapy treatment planning of thoracic cancers. However, this has remained a challenging task for four major reasons: (1) thoracic OARs have diverse morphologies; (2) thoracic OARs have low contrast with the background; (3) boundaries of thoracic OARs are blurry; (4) class imbalance issue caused by small organs. Purpose To overcome the above challenges and achieve accurate and automated segmentation of thoracic OARs on thoracic CT. Methods A novel cascaded framework based on mixed attention and multiscale information for thoracic OARs segmentation, called Cascaded‐TOARNet. This cascaded framework comprises two stages: localization and segmentation. During the localization stage, TOARNet locates each organ to crop the regions of interest (ROIs). During the segmentation stage, TOARNet accurately segments the ROIs, and the segmentation results are merged into a complete result. Results We evaluated our proposed method and other common segmentation methods on two public datasets: the AAPM Thoracic Auto‐Segmentation Challenge dataset and the Segmentation of Thoracic Organs at Risk (SegTHOR) dataset. Our method demonstrated superior performance, achieving a mean Dice score of 92.6% on the SegTHOR dataset and 90.8% on the AAPM dataset. Conclusions This segmentation method holds great promise as an essential tool for enhancing the efficiency of thoracic radiotherapy planning.