{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T19:50:10Z","timestamp":1769716210741,"version":"3.49.0"},"reference-count":8,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,6,1]]},"abstract":"<jats:p>Considering the heterogeneity, diffusive shape, and complex background of tumors, automatic segmentation of hepatic lesions in computed tomography (CT) images has been considered a challenging task. The performance of existing methods remains subject to segmentation uncertainties, especially in tumor boundary regions. The pixel information in these regions will be affected by both sides, thereby exposing the segmentation results to missing marks. To this end, a new network architecture named Two Direction Segmentation U-Net (TDS-U-Net) is hereby designed based on the classic Attention U-Net to tackle this problem. As the most important blocks of the Attention U-Net network, attention gates (AGs) focus on the target structures of different shapes and sizes. In the last layer of TDS-U-Net, two dichotomous convolutional networks are applied to obtain the segmentation maps of the liver and the tumor respectively. Superimposing two segmented maps to obtain the final image addresses the above problems. The entire structure has been verified on two widely accepted public CT datasets, LiTS17 and KiTS19. Compared with the state of the art, this method exhibits superior performance and excellent shape extractions with high detection sensitivity, perfectly demonstrating its effectiveness in medical image segmentation.<\/jats:p>","DOI":"10.3233\/jifs-221111","type":"journal-article","created":{"date-parts":[[2023,3,10]],"date-time":"2023-03-10T12:13:36Z","timestamp":1678450416000},"page":"8817-8825","source":"Crossref","is-referenced-by-count":4,"title":["TDS-U-Net: Automatic liver and tumor separate segmentation of CT volumes using attention gates1"],"prefix":"10.1177","volume":"44","author":[{"given":"Hua","family":"Wang","sequence":"first","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, P. R. China"},{"name":"DewertOKIN Technology Group Co, Ltd, Jiaxing, P. R. China"},{"name":"Bewatec(Zhejiang) Medical Equipment Co., Ltd. Jiaxing, P. R. China"}]},{"given":"Zhi-Ming","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, P. R. China"}]},{"given":"Xiu-Tao","family":"Cui","sequence":"additional","affiliation":[{"name":"DewertOKIN Technology Group Co, Ltd, Jiaxing, P. R. China"},{"name":"Bewatec(Zhejiang) Medical Equipment Co., Ltd. Jiaxing, P. R. China"}]},{"given":"Long","family":"Li","sequence":"additional","affiliation":[{"name":"DewertOKIN Technology Group Co, Ltd, Jiaxing, P. R. China"}]}],"member":"179","reference":[{"issue":"6","key":"10.3233\/JIFS-221111_ref2","first-page":"111","article-title":"Extraction of pig contour based on fully convolutional networks[J]","volume":"39","author":"Hu","year":"2018","journal-title":"Journal of South China Agricultural University"},{"issue":"12","key":"10.3233\/JIFS-221111_ref4","doi-asserted-by":"crossref","first-page":"2663","DOI":"10.1109\/TMI.2018.2845918","article-title":"H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes[J]","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"12","key":"10.3233\/JIFS-221111_ref10","doi-asserted-by":"crossref","first-page":"3820","DOI":"10.1109\/TMI.2021.3098703","article-title":"Efficient medical image segmentation based on knowledge distillation[J]","volume":"40","author":"Qin","year":"2021","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"4","key":"10.3233\/JIFS-221111_ref15","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","article-title":"Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs[J]","volume":"40","author":"Chen","year":"2017","journal-title":"IEEE transactions on pattern analysis and machine intelligence"},{"key":"10.3233\/JIFS-221111_ref16","doi-asserted-by":"crossref","first-page":"103829","DOI":"10.1016\/j.bspc.2022.103829","article-title":"DGCU\u2013Net: A new dual gradient-color deep convolutional neural network for efficient skin lesion segmentation[J]","volume":"77","author":"Ramadan","year":"2022","journal-title":"Biomedical Signal Processing and Control"},{"issue":"6","key":"10.3233\/JIFS-221111_ref20","doi-asserted-by":"crossref","first-page":"1856","DOI":"10.1109\/TMI.2019.2959609","article-title":"Unet++: Redesigning skip connections to exploit multiscale features in image segmentation[J]","volume":"39","author":"Zhou","year":"2019","journal-title":"IEEE Transactions on Medical Imaging"},{"issue":"7639","key":"10.3233\/JIFS-221111_ref21","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1038\/nature21056","article-title":"Dermatologist-level classification of skin cancer with deep neural networks[J]","volume":"542","author":"Esteva","year":"2017","journal-title":"Nature"},{"key":"10.3233\/JIFS-221111_ref24","doi-asserted-by":"crossref","first-page":"106268","DOI":"10.1016\/j.cmpb.2021.106268","article-title":"SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography[J]","volume":"208","author":"Wang","year":"2021","journal-title":"Computer Methods and Programs in Biomedicine"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-221111","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T07:53:37Z","timestamp":1769673217000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-221111"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,1]]},"references-count":8,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-221111","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,1]]}}}