{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,9]],"date-time":"2026-11-09T16:47:41Z","timestamp":1794242861464,"version":"4.9.0"},"reference-count":48,"publisher":"SAGE Publications","issue":"5","license":[{"start":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T00:00:00Z","timestamp":1752710400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61902282"],"award-info":[{"award-number":["61902282"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62071330"],"award-info":[{"award-number":["62071330"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of X-Ray Science and Technology"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:p>Accurate X-ray Computed tomography (CT) image segmentation of the abdominal organs is fundamental for diagnosing abdominal diseases, planning cancer treatment, and formulating radiotherapy strategies. However, the existing deep learning based models for three-dimensional (3D) CT image abdominal multi-organ segmentation face challenges, including complex organ distribution, scarcity of labeled data, and diversity of organ structures, leading to difficulties in model training and convergence and low segmentation accuracy. To address these issues, a novel multi-stage training and a deep supervision model based segmentation approach is proposed. It primary integrates multi-stage training, pseudo- labeling technique, and a developed deep supervision model with attention mechanism (DLAU-Net), specifically designed for 3D abdominal multi-organ segmentation. The DLAU-Net enhances segmentation performance and model adaptability through an improved network architecture. The multi-stage training strategy accelerates model convergence and enhances generalizability, effectively addressing the diversity of abdominal organ structures. The introduction of pseudo-labeling training alleviates the bottleneck of labeled data scarcity and further improves the model's generalization performance and training efficiency. Experiments were conducted on a large dataset provided by the FLARE 2023 Challenge. Comprehensive ablation studies and comparative experiments were conducted to validate the effectiveness of the proposed method. Our method achieves an average organ accuracy (AVG) of 90.5% and a Dice Similarity Coefficient (DSC) of 89.05% and exhibits exceptional performance in terms of training speed and handling data diversity, particularly in the segmentation tasks of critical abdominal organs such as the liver, spleen, and kidneys, significantly outperforming existing comparative methods.<\/jats:p>","DOI":"10.1177\/08953996251355806","type":"journal-article","created":{"date-parts":[[2025,7,17]],"date-time":"2025-07-17T08:35:54Z","timestamp":1752741354000},"page":"998-1011","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":2,"title":["A multi-stage training and deep supervision based segmentation approach for 3D abdominal multi-organ segmentation"],"prefix":"10.1177","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2915-2086","authenticated-orcid":false,"given":"Panpan","family":"Wu","sequence":"first","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0007-9335-3778","authenticated-orcid":false,"given":"Peng","family":"An","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8719-6389","authenticated-orcid":false,"given":"Ziping","family":"Zhao","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1309-5114","authenticated-orcid":false,"given":"Runpeng","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1941-0461","authenticated-orcid":false,"given":"Xiaofeng","family":"Ma","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3968-9317","authenticated-orcid":false,"given":"Yue","family":"Qu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-4451-2774","authenticated-orcid":false,"given":"Yurou","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Computer and Information Engineering, Tianjin Normal University, Tianjin, 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5852-0813","authenticated-orcid":false,"given":"Hengyong","family":"Yu","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,7,17]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"crossref","unstructured":"Ronneberger O Fischer P Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical image computing and computer-assisted intervention\u2013MICCAI 2015: 18th international conference Munich Germany October 5\u20139 2015 proceedings part III 18. Springer 2015 pp. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2017.07.005"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-019-00227-x"},{"key":"e_1_3_3_5_2","unstructured":"Barshan E Fieguth P. Stage-wise training: An improved feature learning strategy for deep models. In: Feature extraction: Modern questions and challenges. PMLR 2015 pp. 49\u201359."},{"key":"e_1_3_3_6_2","volume-title":"Advances in neural information processing systems","author":"Yuan Z","year":"2019","unstructured":"Yuan Z, Yan Y, Jin R, et al. Stagewise training accelerates convergence of testing error over SGD. In: Advances in neural information processing systems. 32. Curran Associates, INC., 2019."},{"key":"e_1_3_3_7_2","unstructured":"Lee DH et al. Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks. In: Workshop on challenges in representation learning ICML. vol. 3. Atlanta 2013 p. 896."},{"key":"e_1_3_3_8_2","volume-title":"Advances in neural information processing systems","author":"Grandvalet Y","year":"2004","unstructured":"Grandvalet Y, Bengio Y. Semi-supervised learning by entropy minimization. In: Advances in neural information processing systems. 17. MIT Press, 2004."},{"key":"e_1_3_3_9_2","doi-asserted-by":"crossref","unstructured":"Kumar V Rao S Yu L. Noisy student training using body language dataset improves facial expression recognition. In: European conference on computer vision. Springer 2020 pp. 756\u2013773.","DOI":"10.1007\/978-3-030-66415-2_53"},{"key":"e_1_3_3_10_2","doi-asserted-by":"crossref","unstructured":"Pham H Dai Z Xie Q et al. Meta pseudo labels. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 2021 pp. 11557\u201311568.","DOI":"10.1109\/CVPR46437.2021.01139"},{"key":"e_1_3_3_11_2","unstructured":"Kumar A Daum\u00e9 H. A co-training approach for multi-view spectral clustering. In: Proceedings of the 28th international conference on machine learning (ICML-11) 2011 pp. 393\u2013400."},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.inffus.2017.02.007"},{"key":"e_1_3_3_13_2","doi-asserted-by":"crossref","unstructured":"Blum A Mitchell T. Combining labeled and unlabeled data with co-training. In: Proceedings of the eleventh annual conference on Computational learning theory. 1998 pp. 92\u2013100.","DOI":"10.1145\/279943.279962"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TSG.2020.3010510"},{"key":"e_1_3_3_15_2","doi-asserted-by":"crossref","unstructured":"Valindria VV Pawlowski N Rajchl M et al. Multi-modal learning from unpaired images: Application to multi-organ segmentation in CT and MRI. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE 2018 pp. 547\u2013556.","DOI":"10.1109\/WACV.2018.00066"},{"key":"e_1_3_3_16_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3001036"},{"key":"e_1_3_3_17_2","doi-asserted-by":"crossref","unstructured":"Zhou Z Rahman Siddiquee MM Tajbakhsh N et al. Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support: 4th international workshop DLMIA 2018 and 8th international workshop ML-CDS 2018 held in conjunction with MICCAI 2018 Granada Spain September 20 2018 Proceedings 4. Springer 2018 pp. 3\u201311.","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"e_1_3_3_18_2","doi-asserted-by":"crossref","unstructured":"Milletari F Navab N Ahmadi SA. V-net: Fully convolutional neural networks for volumetric medical image segmentation. In: 2016 fourth international conference on 3D vision (3DV). IEEE 2016 pp. 565\u2013571.","DOI":"10.1109\/3DV.2016.79"},{"key":"e_1_3_3_19_2","doi-asserted-by":"crossref","unstructured":"Isensee F Petersen J Klein A et al. nnu-net: Self-adapting framework for u-net-based medical image segmentation. arXiv preprint arXiv:180910486. 2018.","DOI":"10.1007\/978-3-658-25326-4_7"},{"key":"e_1_3_3_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.isprsjprs.2020.01.013"},{"key":"e_1_3_3_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.optcom.2021.126970"},{"key":"e_1_3_3_22_2","doi-asserted-by":"crossref","unstructured":"Yu F Wang D Shelhamer E et al. Deep layer aggregation. In: Proceedings of the IEEE conference on computer vision and pattern recognition 2018 pp. 2403\u20132412.","DOI":"10.1109\/CVPR.2018.00255"},{"key":"e_1_3_3_23_2","unstructured":"Lee CY Xie S Gallagher P et al. Deeply-supervised nets. In: Artificial intelligence and statistics. Pmlr 2015 pp. 562\u2013570."},{"key":"e_1_3_3_24_2","unstructured":"Wang L Lee CY Tu Z et al. Training deeper convolutional networks with deep supervision. arXiv preprint arXiv:150502496. 2015."},{"key":"e_1_3_3_25_2","doi-asserted-by":"crossref","unstructured":"Zhu Q Du B Turkbey B et al. Deeply-supervised CNN for prostate segmentation. In: 2017 international joint conference on neural networks (IJCNN). IEEE 2017 pp. 178\u2013184.","DOI":"10.1109\/IJCNN.2017.7965852"},{"key":"e_1_3_3_26_2","doi-asserted-by":"crossref","unstructured":"Zhang W Zhu L Hallinan J et al. Boostmis: Boosting medical image semi-supervised learning with adaptive pseudo labeling and informative active annotation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition 2022 pp. 20666\u201320676.","DOI":"10.1109\/CVPR52688.2022.02001"},{"key":"e_1_3_3_27_2","doi-asserted-by":"crossref","unstructured":"Arazo E Ortego D Albert P et al. Pseudo-labeling and confirmation bias in deep semi-supervised learning. In: 2020 International joint conference on neural networks (IJCNN). IEEE 2020 pp. 1\u20138.","DOI":"10.1109\/IJCNN48605.2020.9207304"},{"key":"e_1_3_3_28_2","unstructured":"Tao A Sapra K Catanzaro B. Hierarchical multi-scale attention for semantic segmentation. arXiv preprint arXiv:200510821. 2020."},{"key":"e_1_3_3_29_2","unstructured":"Simonyan K Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014."},{"key":"e_1_3_3_30_2","unstructured":"Oktay O Schlemper J Folgoc LL et al. Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:180403999. 2018."},{"key":"e_1_3_3_31_2","doi-asserted-by":"crossref","unstructured":"Xie S Tu Z. Holistically-nested edge detection. In: Proceedings of the IEEE international conference on computer vision. 2015 pp. 1395\u20131403.","DOI":"10.1109\/ICCV.2015.164"},{"key":"e_1_3_3_32_2","doi-asserted-by":"publisher","DOI":"10.1109\/TMI.2020.3046579"},{"key":"e_1_3_3_33_2","first-page":"84","volume-title":"MICCAI Challenge on fast and low-resource semi-supervised abdominal organ segmentation","author":"Lyu P","year":"2023","unstructured":"Lyu P, Xiong J, Fang W, et al. Advancing multi-organ and pan- cancer segmentation in abdominal CT scans through scale-aware and self-attentive modulation. In: MICCAI Challenge on fast and low-resource semi-supervised abdominal organ segmentation. Cham: Springer, 2023, pp.84\u2013101."},{"key":"e_1_3_3_34_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102616"},{"key":"e_1_3_3_35_2","doi-asserted-by":"crossref","unstructured":"Ma J Zhang Y Gu S et al. Unleashing the strengths of unlabeled data in pan-cancer abdominal organ quantification: the flare22 challenge. arXiv preprint arXiv:230805862. 2023.","DOI":"10.1016\/S2589-7500(24)00154-7"},{"key":"e_1_3_3_36_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10278-013-9622-7"},{"key":"e_1_3_3_37_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2022.102680"},{"key":"e_1_3_3_38_2","unstructured":"Simpson AL Antonelli M Bakas S et al. A large annotated medical image dataset for the development and evaluation of segmentation algorithms. arXiv preprint arXiv:190209063. 2019."},{"key":"e_1_3_3_39_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2020.101821"},{"key":"e_1_3_3_40_2","doi-asserted-by":"publisher","DOI":"10.1200\/JCO.2020.38.6_suppl.626"},{"key":"e_1_3_3_41_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41597-022-01718-3"},{"key":"e_1_3_3_42_2","doi-asserted-by":"publisher","DOI":"10.1038\/s42256-024-00912-9"},{"key":"e_1_3_3_43_2","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.230024"},{"key":"e_1_3_3_44_2","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2021.3100536"},{"key":"e_1_3_3_45_2","doi-asserted-by":"crossref","unstructured":"Yushkevich PA Gao Y Gerig G. ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images. In: 2016 38th annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE 2016 pp. 3342\u20133345.","DOI":"10.1109\/EMBC.2016.7591443"},{"key":"e_1_3_3_46_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-020-01008-z"},{"key":"e_1_3_3_47_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-024-44824-z"},{"key":"e_1_3_3_48_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2024.103280"},{"key":"e_1_3_3_49_2","doi-asserted-by":"crossref","unstructured":"Cao H Wang Y Chen J et al. Swin-unet: Unet-like pure transformer for medical image segmentation. In: European conference on computer vision. Springer 2022 pp. 205\u2013218.","DOI":"10.1007\/978-3-031-25066-8_9"}],"container-title":["Journal of X-Ray Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996251355806","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/08953996251355806","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/08953996251355806","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T22:07:28Z","timestamp":1777673248000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/08953996251355806"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,17]]},"references-count":48,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1177\/08953996251355806"],"URL":"https:\/\/doi.org\/10.1177\/08953996251355806","relation":{},"ISSN":["0895-3996","1095-9114"],"issn-type":[{"value":"0895-3996","type":"print"},{"value":"1095-9114","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,17]]}}}