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This paper proposes a parallel\u2010path attention fusion network (PPAF\u2010net) to overcome these disadvantages in the delineation\u00a0task.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The PPAF\u2010net utilizes both the texture and structure information of CTV and OARs by employing a U\u2010Net network to capture the high\u2010level texture information, and an up\u2010sampling and down\u2010sampling (USDS) network to capture the low\u2010level structure information to accentuate the boundaries of CTV and OARs. Multi\u2010level features extracted from both networks are then fused together through an attention module to generate the delineation\u00a0result.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The dataset contains 276 computed tomography (CT) scans of patients with cervical cancer of staging IB\u2010IIA. The images are provided by the West China Hospital of Sichuan University. Simulation results demonstrate that PPAF\u2010net performs favorably on the delineation of the CTV and OARs (e.g., rectum, bladder and etc.) and achieves the state\u2010of\u2010the\u2010art delineation accuracy, respectively, for the CTV and OARs. In terms of the Dice Similarity Coefficient (DSC) and the Hausdorff Distance (HD), 88.61% and 2.25 cm for the CTV, 92.27% and 0.73 cm for the rectum, 96.74% and 0.68 cm for the bladder, 96.38% and 0.65 cm for the left kidney, 96.79% and 0.63 cm for the right kidney, 93.42% and 0.52 cm for the left femoral head, 93.69% and 0.51 cm for the right femoral head, 87.53% and 1.07 cm for the small intestine, and 91.50% and 0.84 cm for the spinal\u00a0cord.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>The proposed automatic delineation network PPAF\u2010net performs well on CTV and OARs segmentation tasks, which has great potential for reducing the burden of radiation oncologists and increasing the accuracy of delineation. In future, radiation oncologists from the West China Hospital of Sichuan University will further evaluate the results of network delineation, making this method helpful in clinical\u00a0practice.<\/jats:p><\/jats:sec>","DOI":"10.1002\/mp.16468","type":"journal-article","created":{"date-parts":[[2023,5,29]],"date-time":"2023-05-29T09:39:27Z","timestamp":1685353167000},"page":"6354-6365","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Delineation of clinical target volume and organs at risk in cervical cancer radiotherapy by deep learning networks"],"prefix":"10.1002","volume":"50","author":[{"given":"Miao","family":"Tian","sequence":"first","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongqiu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xingang","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuyun","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering University of Tulsa Tulsa USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ganlu","family":"Ouyang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cancer Center the West China Hospital of Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yali","family":"Shen","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cancer Center the West China Hospital of Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhiping","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cancer Center the West China Hospital of Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Wang","sequence":"additional","affiliation":[{"name":"Department of Radiation Oncology, Cancer Center the West China Hospital of Sichuan University Chengdu China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaozhi","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China"},{"name":"Yangtze Delta Region Institute (Quzhou) University of Electronic Science and Technology of China Quzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2023,5,29]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21492"},{"key":"e_1_2_8_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(19)30482-6"},{"key":"e_1_2_8_4_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660"},{"key":"e_1_2_8_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.semradonc.2009.11.006"},{"key":"e_1_2_8_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/cncr.21324"},{"key":"e_1_2_8_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijrobp.2009.10.075"},{"key":"e_1_2_8_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2015.10.007"},{"key":"e_1_2_8_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.radonc.2016.09.009"},{"key":"e_1_2_8_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/S1470-2045(19)30308-0"},{"key":"e_1_2_8_11_1","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2015.2433397"},{"key":"e_1_2_8_12_1","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2012.2186976"},{"key":"e_1_2_8_13_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2011.05.010"},{"key":"e_1_2_8_14_1","doi-asserted-by":"crossref","unstructured":"MilletariF NavabN AhmadiSA.V\u2010Net: Fully convolutional neural networks for volumetric medical image segmentation. 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