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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\u2010to\u2010segment 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\u2010layer attention fusion network (CLAF\u2010CNN) to solve the problem of accurately segmenting\u00a0OARs.<\/jats:p><jats:p><jats:bold>Methods<\/jats:bold>: In CLAF\u2010CNN, 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\u2010related features. In this way, the spatial attention modules in the network can be learned and optimized together. In addition, we introduce a new Top\u2010K exponential logarithmic Dice loss (TELD\u2010Loss) to solve the imbalance problem in OAR segmentation. The TELD\u2010Loss further introduces a Top\u2010K optimization mechanism based on Dice loss and exponential logarithmic loss, which makes the network pay more attention to small organs and difficult\u2010to\u2010segment organs, so as to enhance the overall performance of the segmentation\u00a0model.<\/jats:p><jats:p><jats:bold>Results<\/jats:bold>: 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\u2010CNN outperforms the state\u2010of\u2010the\u2010art attention\u2010based segmentation methods in the OAR segmentation task with average Dice coefficient of 79.65% for head and neck OARs and 88.39% for lung\u00a0OARs.<\/jats:p><jats:p><jats:bold>Conclusions<\/jats:bold>: This work provides a new network named CLAF\u2010CNN which contains cross\u2010layer spatial attention map fusion architecture and TELD\u2010Loss 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.<\/jats:p>","DOI":"10.1002\/mp.15260","type":"journal-article","created":{"date-parts":[[2021,10,6]],"date-time":"2021-10-06T21:40:48Z","timestamp":1633556448000},"page":"6987-7002","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Automatic segmentation of organs\u2010at\u2010risks of nasopharynx cancer and lung cancer by cross\u2010layer attention fusion network with TELD\u2010Loss"],"prefix":"10.1002","volume":"48","author":[{"given":"Zuhao","family":"Liu","sequence":"first","affiliation":[{"name":"Glasgow College University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chao","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Radiology Peking University People's Hospital  Beijing 100044 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation Engineering University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yibo","family":"Gao","sequence":"additional","affiliation":[{"name":"Glasgow College University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shichuan","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology Sichuan Cancer Hospital and Institute University of Electronic Science and Technology of China  Chengdu 610041 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guotai","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu 611731 China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaoting","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical and Electrical Engineering University of Electronic Science and Technology of China  Chengdu 611731 China"},{"name":"SenseTime Research  Shanghai 200233 China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2021,10,18]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11547-013-0359-7"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.clon.2011.06.006"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.09.062"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1111\/1754-9485.12462"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2006.12.039"},{"key":"e_1_2_9_7_1","doi-asserted-by":"crossref","unstructured":"GaoY HuangR ChenM et\u00a0al.Focusnet: imbalanced large and small organ segmentation with an end\u2010to\u2010end deep neural network for head and neck ct images. 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