{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T14:03:45Z","timestamp":1774965825062,"version":"3.50.1"},"reference-count":36,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Open Project of Network and Data Security Key Laboratory of Sichuan Province","award":["NSD2021-6"],"award-info":[{"award-number":["NSD2021-6"]}]},{"name":"Clinical Research and Transformation Fund of Sichuan Provincial People's Hospital","award":["2021LY24"],"award-info":[{"award-number":["2021LY24"]}]},{"name":"the Key Research Project of Science and Technology of Sichuan Province","award":["2022YFS0087, 2023YFS0039"],"award-info":[{"award-number":["2022YFS0087, 2023YFS0039"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-00981-7","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T20:01:51Z","timestamp":1709582511000},"page":"1529-1547","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":138,"title":["From CNN to Transformer: A Review of Medical Image Segmentation Models"],"prefix":"10.1007","volume":"37","author":[{"given":"Wenjian","family":"Yao","sequence":"first","affiliation":[]},{"given":"Jiajun","family":"Bai","sequence":"additional","affiliation":[]},{"given":"Wei","family":"Liao","sequence":"additional","affiliation":[]},{"given":"Yuheng","family":"Chen","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3679-7915","authenticated-orcid":false,"given":"Mengjuan","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Yao","family":"Xie","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"981_CR1","doi-asserted-by":"crossref","unstructured":"Cheng, J.Z., Ni, D., Chou, Y.H., Qin, J., Tiu, C.M., Chang, Y.C., Huang, C.S., Shen, D., Chen, C.M.: Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Scientific Reports 6, 24454 (2016)","DOI":"10.1038\/srep24454"},{"key":"981_CR2","doi-asserted-by":"crossref","unstructured":"Golan, R., Jacob, C., Denzinger, J.: Lung nodule detection in ct images using deep convolutional neural networks. In: International Joint Conference on Neural Networks (2016)","DOI":"10.1109\/IJCNN.2016.7727205"},{"key":"981_CR3","unstructured":"Christ, P.F., Ettlinger, F., Gr\u00fcn, F., Elshaera, M.E.A., Lipkova, J., Schlecht, S., Ahmaddy, F., Tatavarty, S., Bickel, M., Bilic, P.: Automatic liver and tumor segmentation of CT and MRI volumes using cascaded fully convolutional neural networks (2017)"},{"key":"981_CR4","doi-asserted-by":"crossref","unstructured":"Otsu, N.: A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man, and Cybernetics 9(1), 62\u201366 (1979)","DOI":"10.1109\/TSMC.1979.4310076"},{"key":"981_CR5","doi-asserted-by":"crossref","unstructured":"Magnier, Baptiste: Edge detection: a review of dissimilarity evaluations and a proposed normalized measure. Multimedia Tools & Applications (2017)","DOI":"10.1007\/s11042-017-5127-6"},{"key":"981_CR6","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, pp. 234\u2013241 (2015). Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"981_CR7","doi-asserted-by":"crossref","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39(12), 2481\u20132495 (2017)","DOI":"10.1109\/TPAMI.2016.2644615"},{"key":"981_CR8","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFS. arXiv preprint arXiv:1412.7062 (2014)"},{"key":"981_CR9","unstructured":"Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., Lu, L., Yuille, A.L., Zhou, Y.: Transunet: Transformers make strong encoders for medical image segmentation. arXiv preprint arXiv:2102.04306 (2021)"},{"key":"981_CR10","doi-asserted-by":"crossref","unstructured":"Cao, H., Wang, Y., Chen, J., Jiang, D., Zhang, X., Tian, Q., Wang, M.: Swin-unet: Unet-like pure transformer for medical image segmentation. In: European Conference on Computer Vision, pp. 205\u2013218 (2022). Springer","DOI":"10.1007\/978-3-031-25066-8_9"},{"key":"981_CR11","doi-asserted-by":"crossref","unstructured":"Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision, pp. 213\u2013229 (2020). Springer","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"981_CR12","doi-asserted-by":"crossref","unstructured":"Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022 (2021)","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"981_CR13","doi-asserted-by":"crossref","unstructured":"Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Xiao, T., Whitehead, S., Berg, A.C., Lo, W.-Y., et al.: Segment anything. arXiv preprint arXiv:2304.02643 (2023)","DOI":"10.1109\/ICCV51070.2023.00371"},{"key":"981_CR14","doi-asserted-by":"crossref","unstructured":"Aljuaid, A., Anwar, M.: Survey of supervised learning for medical image processing. SN Computer Science 3(4), 292 (2022)","DOI":"10.1007\/s42979-022-01166-1"},{"key":"981_CR15","doi-asserted-by":"crossref","unstructured":"Abdou, M.A.: Literature review: Efficient deep neural networks techniques for medical image analysis. Neural Computing and Applications 34(8), 5791\u20135812 (2022)","DOI":"10.1007\/s00521-022-06960-9"},{"key":"981_CR16","doi-asserted-by":"crossref","unstructured":"Asgari\u00a0Taghanaki, S., Abhishek, K., Cohen, J.P., Cohen-Adad, J., Hamarneh, G.: Deep semantic segmentation of natural and medical images: a review. Artificial Intelligence Review 54, 137\u2013178 (2021)","DOI":"10.1007\/s10462-020-09854-1"},{"key":"981_CR17","doi-asserted-by":"crossref","unstructured":"Zhou, Z., Rahman\u00a0Siddiquee, M.M., Tajbakhsh, N., Liang, J.: 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, pp. 3\u201311 (2018). Springer","DOI":"10.1007\/978-3-030-00889-5_1"},{"key":"981_CR18","doi-asserted-by":"crossref","unstructured":"Ker, J., Wang, L., Rao, J., Lim, T.: Deep learning applications in medical image analysis. Ieee Access 6, 9375\u20139389 (2017)","DOI":"10.1109\/ACCESS.2017.2788044"},{"key":"981_CR19","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25 (2012)"},{"key":"981_CR20","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"981_CR21","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. IEEE transactions on pattern analysis and machine intelligence 40(4), 834\u2013848 (2017)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"981_CR22","unstructured":"Chen, L.-C., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. arXiv preprint arXiv:1706.05587 (2017)"},{"key":"981_CR23","doi-asserted-by":"crossref","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801\u2013818 (2018)","DOI":"10.1007\/978-3-030-01234-2_49"},{"key":"981_CR24","unstructured":"Yu, F., Koltun, V.: Multi-scale context aggregation by dilated convolutions. arXiv preprint arXiv:1511.07122 (2015)"},{"key":"981_CR25","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. IEEE transactions on pattern analysis and machine intelligence 37(9), 1904\u20131916 (2015)","DOI":"10.1109\/TPAMI.2015.2389824"},{"key":"981_CR26","doi-asserted-by":"crossref","unstructured":"Li, C., Tan, Y., Chen, W., Luo, X., Gao, Y., Jia, X., Wang, Z.: Attention unet++: A nested attention-aware u-net for liver ct image segmentation. In: 2020 IEEE International Conference on Image Processing (ICIP), pp. 345\u2013349 (2020). IEEE","DOI":"10.1109\/ICIP40778.2020.9190761"},{"key":"981_CR27","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der\u00a0Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"981_CR28","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Doll\u00e1r, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 16000\u201316009 (2022)","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"981_CR29","unstructured":"Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning, pp. 8748\u20138763 (2021). PMLR"},{"key":"981_CR30","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, \u0141., Polosukhin, I.: Attention is all you need. Advances in neural information processing systems 30 (2017)"},{"key":"981_CR31","unstructured":"Jaeger, S., Candemir, S., Antani, S., W\u00e1ng, Y.-X.J., Lu, P.-X., Thoma, G.: Two public chest x-ray datasets for computer-aided screening of pulmonary diseases. Quantitative imaging in medicine and surgery 4(6), 475 (2014)"},{"key":"981_CR32","doi-asserted-by":"crossref","unstructured":"Heimann, T., Van\u00a0Ginneken, B., Styner, M.A., Arzhaeva, Y., Aurich, V., Bauer, C., Beck, A., Becker, C., Beichel, R., Bekes, G., et al: Comparison and evaluation of methods for liver segmentation from ct datasets. IEEE transactions on medical imaging 28(8), 1251\u20131265 (2009)","DOI":"10.1109\/TMI.2009.2013851"},{"key":"981_CR33","doi-asserted-by":"crossref","unstructured":"Milletari, F., Ahmadi, S.-A., Kroll, C., Plate, A., Rozanski, V., Maiostre, J., Levin, J., Dietrich, O., Ertl-Wagner, B., B\u00f6tzel, K., et al: Hough-CNN: Deep learning for segmentation of deep brain regions in MRI and ultrasound. Computer Vision and Image Understanding 164, 92\u2013102 (2017)","DOI":"10.1016\/j.cviu.2017.04.002"},{"key":"981_CR34","doi-asserted-by":"crossref","unstructured":"Golan, R., Jacob, C., Denzinger, J.: Lung nodule detection in ct images using deep convolutional neural networks. In: 2016 International Joint Conference on Neural Networks (IJCNN), pp. 243\u2013250 (2016). IEEE","DOI":"10.1109\/IJCNN.2016.7727205"},{"key":"981_CR35","doi-asserted-by":"crossref","unstructured":"Beevi, K.S., Nair, M.S., Bindu, G.: Automatic mitosis detection in breast histopathology images using convolutional neural network based deep transfer learning. Biocybernetics and Biomedical Engineering 39(1), 214\u2013223 (2019)","DOI":"10.1016\/j.bbe.2018.10.007"},{"key":"981_CR36","unstructured":"Urban, G., Bendszus, M., Hamprecht, F., Kleesiek, J., et al.: Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, winning contribution, 31\u201335 (2014)"}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-00981-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-00981-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-00981-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,5]],"date-time":"2024-08-05T17:18:07Z","timestamp":1722878287000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-00981-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,4]]},"references-count":36,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["981"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-00981-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,4]]},"assertion":[{"value":"10 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 November 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"4 March 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of Sichuan Provincial People\u2019s Hospital.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"Informed consent was obtained from all individual participants included in the study.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to Participate"}},{"value":"The authors affirm that patients signed informed consent regarding publishing their data and photographs. <b>Tuberculosis Chest X-rays dataset<\/b> is from the publicly available dataset: . <b>Clinical Liver CT dataset<\/b> is from the publicly available dataset: . <b>Ovarian Tumors dataset<\/b>, we obtained all the informed consent. Also, the patient\u2019s abdominal images were anonymized so that the images would not identify a patient.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for Publication"}},{"value":"The authors declare no competing interests.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}