Segmentation of multiple Organs‐at‐Risk associated with brain tumors based on coarse‐to‐fine stratified networks.

Background: Delineation of Organs‐at‐Risks (OARs) is an important step in radiotherapy treatment planning. As manual delineation is time‐consuming, labor‐intensive and affected by inter‐ and intra‐observer variability, a robust and efficient automatic segmentation algorithm is highly desirable for improving the efficiency and repeatability of OAR delineation. Purpose: Automatic segmentation of OARs in medical images is challenged by low contrast, various shapes and imbalanced sizes of different organs. We aim to overcome these challenges and develop a high‐performance method for automatic segmentation of 10 OARs required in radiotherapy planning for brain tumors. Methods: A novel two‐stage segmentation framework is proposed, where a coarse and simultaneous localization of all the target organs is obtained in the first stage, and a fine segmentation is achieved for each organ, respectively, in the second stage. To deal with organs with various sizes and shapes, a stratified segmentation strategy is proposed, where a High‐ and Low‐Resolution Residual Network (HLRNet) that consists of a multiresolution branch and a high‐resolution branch is introduced to segment medium‐sized organs, and a High‐Resolution Residual Network (HRRNet) is used to segment small organs. In addition, a label fusion strategy is proposed to better deal with symmetric pairs of organs like the left and right cochleas and lacrimal glands. Results: Our method was validated on the dataset of MICCAI ABCs 2020 challenge for OAR segmentation. It obtained an average Dice of 75.8% for 10 OARs, and significantly outperformed several state‐of‐the‐art models including nnU‐Net (71.6%) and FocusNet (72.4%). Our proposed HLRNet and HRRNet improved the segmentation accuracy for medium‐sized and small organs, respectively. The label fusion strategy led to higher accuracy for symmetric pairs of organs. Conclusions: Our proposed method is effective for the segmentation of OARs of brain tumors, with a better performance than existing methods, especially on medium‐sized and small organs. It has a potential for improving the efficiency of radiotherapy planning with high segmentation accuracy. [ABSTRACT FROM AUTHOR]