{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T04:19:44Z","timestamp":1783570784575,"version":"3.55.0"},"reference-count":45,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,21]],"date-time":"2024-08-21T00:00:00Z","timestamp":1724198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Liver segmentation technologies play vital roles in clinical diagnosis, disease monitoring, and surgical planning due to the complex anatomical structure and physiological functions of the liver. This paper provides a comprehensive review of the developments, challenges, and future directions in liver segmentation technology. We systematically analyzed high-quality research published between 2014 and 2024, focusing on liver segmentation methods, public datasets, and evaluation metrics. This review highlights the transition from manual to semi-automatic and fully automatic segmentation methods, describes the capabilities and limitations of available technologies, and provides future outlooks.<\/jats:p>","DOI":"10.3390\/jimaging10080202","type":"journal-article","created":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T04:26:57Z","timestamp":1724300817000},"page":"202","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":18,"title":["A Review of Advancements and Challenges in Liver Segmentation"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-2498-9534","authenticated-orcid":false,"given":"Di","family":"Wei","sequence":"first","affiliation":[{"name":"Department of Radiology, The Eighth Affiliated Hospital of The Sun Yat-sen University, No. 3025, Middle Shennan Road, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yundan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Eighth Affiliated Hospital of The Sun Yat-sen University, No. 3025, Middle Shennan Road, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1785-3969","authenticated-orcid":false,"given":"Xuhui","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Eighth Affiliated Hospital of The Sun Yat-sen University, No. 3025, Middle Shennan Road, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Di","family":"Wu","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Eighth Affiliated Hospital of The Sun Yat-sen University, No. 3025, Middle Shennan Road, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-3978-1622","authenticated-orcid":false,"given":"Xiaorong","family":"Feng","sequence":"additional","affiliation":[{"name":"Department of Radiology, The Eighth Affiliated Hospital of The Sun Yat-sen University, No. 3025, Middle Shennan Road, Shenzhen 518033, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1159\/000018770","article-title":"Liver anatomy: Portal and suprahepatic or biliary segmentation","volume":"16","author":"Couinaud","year":"1999","journal-title":"Dig. Surg."},{"key":"ref_2","unstructured":"Gonzalez, R.C., and Woods, R.E. (2018). Digital Image Process., Pearson."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s13244-017-0558-1","article-title":"Liver segmentation: Indications, techniques and future directions","volume":"8","author":"Gotra","year":"2017","journal-title":"Insights Imaging"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"102680","DOI":"10.1016\/j.media.2022.102680","article-title":"The Liver Tumor Segmentation Benchmark (LiTS)","volume":"84","author":"Bilic","year":"2023","journal-title":"Med. Image Anal."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Ni\u00f1o, S.B., Bernardino, J., and Domingues, I. (2024). Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors, 24.","DOI":"10.20944\/preprints202402.0464.v1"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1007\/s10278-019-00262-8","article-title":"Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters","volume":"33","author":"Alirr","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"6370","DOI":"10.1038\/s41598-022-09978-0","article-title":"Deep 3D attention CLSTM U-Net based automated liver segmentation and volumetry for the liver transplantation in abdominal CT volumes","volume":"12","author":"Jeong","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1186\/s12880-022-00825-2","article-title":"Practical utility of liver segmentation methods in clinical surgeries and interventions","volume":"22","author":"Ansari","year":"2022","journal-title":"BMC Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.jhep.2023.01.006","article-title":"Artificial intelligence, machine learning, and deep learning in liver transplantation","volume":"78","author":"Bhat","year":"2023","journal-title":"J. Hepatol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"15794","DOI":"10.1038\/s41598-022-20108-8","article-title":"A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams","volume":"12","author":"Senthilvelan","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_11","unstructured":"Bilic, P., Christ, P., Li, H.B., and Vorontsov, E. (2024, July 10). LiTS (Liver Tumor Segmentation Challenge). Available online: https:\/\/competitions.codalab.org\/competitions\/17094."},{"key":"ref_12","unstructured":"Soler, L., Hostettler, A., Agnus, V., Charnoz, A., Fasquel, J., Moreau, J., Osswald, A., Bouhadjar, M., and Marescaux, J. (2010). 3D Image Reconstruction for Comparison of Algorithm Database: A Patient Specific Anatomical and Medical Image Database, IRCAD."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.3934\/mbe.2023059","article-title":"Multi-scale attention and deep supervision-based 3D UNet for automatic liver segmentation from CT","volume":"20","author":"Wang","year":"2023","journal-title":"Math. Biosci. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4327","DOI":"10.3934\/mbe.2021217","article-title":"Liver vessel segmentation based on inter-scale V-Net","volume":"18","author":"Yang","year":"2021","journal-title":"Math. Biosci. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"2110","DOI":"10.1109\/TBME.2016.2631139","article-title":"Liver Segmentation on CT and MR Using Laplacian Mesh Optimization","volume":"64","author":"Chartrand","year":"2017","journal-title":"IEEE Trans. Bio-Med. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"5315","DOI":"10.1109\/TIP.2015.2481326","article-title":"Automatic Liver Segmentation Based on Shape Constraints and Deformable Graph Cut in CT Images","volume":"24","author":"Li","year":"2015","journal-title":"IEEE Trans. Image Process. Publ. IEEE Signal Process. Soc."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"100789","DOI":"10.1016\/j.irbm.2023.100789","article-title":"MCFA-UNet: Multiscale Cascaded Feature Attention U-Net for Liver Segmentation","volume":"44","author":"Zhou","year":"2023","journal-title":"IRBM"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Quinton, F., Popoff, R., Presles, B., Leclerc, S., Meriaudeau, F., Nodari, G., Lopez, O., Pellegrinelli, J., Chevallier, O., and Ginhac, D. (2023). A Tumour and Liver Automatic Segmentation (ATLAS) Dataset on Contrast-Enhanced Magnetic Resonance Imaging for Hepatocellular Carcinoma. Data, 8.","DOI":"10.3390\/data8050079"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Hossain, M.S.A., Gul, S., Chowdhury, M.E.H., Khan, M.S., Sumon, M.S.I., Bhuiyan, E.H., Khandakar, A., Hossain, M., Sadique, A., and Al-Hashimi, I. (2023). Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images. Sensors, 23.","DOI":"10.3390\/s23218890"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101950","DOI":"10.1016\/j.media.2020.101950","article-title":"CHAOS Challenge\u2014combined (CT-MR) healthy abdominal organ segmentation","volume":"69","author":"Kavur","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_21","unstructured":"(2024, July 10). Mark and Mary Stevens Neuroimaging and Informatics Institute, University of Southern California, ATLAS v2.0 Dataset. Used under Creative Commons Attribution 4.0 International License (CC-BY 4.0). Available online: https:\/\/opendata.atlas.cern\/docs\/documentation\/overview_data\/."},{"key":"ref_22","unstructured":"Ali Emre Kavur, M., Alper, S., O\u011fuz, D., Mustafa, B., and Sinem Gezer, N. (2019). (CHAOS\u2014Combined (CT-MR) Healthy Abdominal Organ Segmentation Challenge Data (Version v1.03) [Data set]. Zenodo."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Rafiei, S., Karimi, N., Mirmahboub, B., Najarian, K., Felfeliyan, B., Samavi, S., and Reza Soroushmehr, S.M. (2019, January 23\u201327). Liver Segmentation in Abdominal CT Images Using Probabilistic Atlas and Adaptive 3D Region Growing. Proceedings of the 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Berlin, Germany.","DOI":"10.1109\/EMBC.2019.8857835"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"\u00d6zcan, F., U\u00e7an, O.N., Kara\u00e7am, S., and Tun\u00e7man, D. (2023). Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet. Bioengineering, 10.","DOI":"10.3390\/bioengineering10020215"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully Convolutional Networks for Semantic Segmentation","volume":"39","author":"Shelhamer","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_28","first-page":"9351","article-title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","volume":"Volume 9351","author":"Navab","year":"2015","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2015, Proceedings of the 18th International Conference, Munich, Germany, 5\u20139 October 2015"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"180022","DOI":"10.1148\/ryai.2019180022","article-title":"Automated CT and MRI Liver Segmentation and Biometry Using a Generalized Convolutional Neural Network","volume":"1","author":"Wang","year":"2019","journal-title":"Radiology Artif. Intell."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"987","DOI":"10.3348\/kjr.2020.0237","article-title":"Deep Learning Algorithm for Automated Segmentation and Volume Measurement of the Liver and Spleen Using Portal Venous Phase Computed Tomography Images","volume":"21","author":"Ahn","year":"2020","journal-title":"Korean J. Radiol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1186\/s13014-019-1392-z","article-title":"Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer","volume":"14","author":"Ahn","year":"2019","journal-title":"Radiat. Oncol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Ayalew, Y.A., Fante, K.A., and Mohammed, M.A. (2021). Modified U-Net for liver cancer segmentation from computed tomography images with a new class balancing method. BMC Biomed. Eng., 3.","DOI":"10.1186\/s42490-021-00050-y"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der, M., and Weinberger, K.Q. (2017, January 21\u201326). Densely Connected Convolutional Networks. Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_34","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","volume":"37","author":"Li","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Luan, S., Xue, X., Ding, Y., Wei, W., and Zhu, B. (2021). Adaptive Attention Convolutional Neural Network for Liver Tumor Segmentation. Front. Oncol., 11.","DOI":"10.3389\/fonc.2021.680807"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","article-title":"SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation","volume":"39","author":"Badrinarayanan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1822","DOI":"10.1109\/TMI.2018.2806309","article-title":"Automatic Multi-Organ Segmentation on Abdominal CT with Dense V-Networks","volume":"37","author":"Gibson","year":"2018","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Milletari, F., Navab, N., and Ahmadi, S.A. (2016, January 25\u201328). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA, USA.","DOI":"10.1109\/3DV.2016.79"},{"key":"ref_39","first-page":"9901","article-title":"3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation","volume":"Volume 9901","author":"Ourselin","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Proceedings of the 9th International Conference, Athens, Greece, 17\u201321 October 2016"},{"key":"ref_40","first-page":"9901","article-title":"3D Deeply Supervised Network for Automatic Liver Segmentation from CT Volumes","volume":"Volume 9901","author":"Ourselin","year":"2016","journal-title":"Medical Image Computing and Computer-Assisted Intervention\u2014MICCAI 2016, Proceedings of the 9th International Conference, Athens, Greece, 17\u201321 October 2016"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"014006","DOI":"10.1117\/1.JMI.6.1.014006","article-title":"Recurrent residual U-Net for medical image segmentation","volume":"6","author":"Alom","year":"2019","journal-title":"J. Med. Imaging"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"102444","DOI":"10.1016\/j.media.2022.102444","article-title":"Recent advances and clinical applications of deep learning in medical image analysis","volume":"79","author":"Chen","year":"2022","journal-title":"Med. Image Anal."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Siam, A., Alsaify, A.R., Mohammad, B., Biswas, M.R., Ali, H., and Shah, Z. (2023). Multimodal deep learning for liver cancer applications: A scoping review. Front. Artif. Intell., 6.","DOI":"10.3389\/frai.2023.1247195"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1186\/s13550-019-0485-x","article-title":"Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization","volume":"9","author":"Coudyzer","year":"2019","journal-title":"EJNMMI Res."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.compmedimag.2018.03.001","article-title":"An application of cascaded 3D fully convolutional networks for medical image segmentation","volume":"66","author":"Roth","year":"2018","journal-title":"Comput. Med. Imaging Graph. Off. J. Comput. Med. Imaging Soc."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/8\/202\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:40:14Z","timestamp":1760110814000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/10\/8\/202"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,21]]},"references-count":45,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["jimaging10080202"],"URL":"https:\/\/doi.org\/10.3390\/jimaging10080202","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,21]]}}}