{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/automatic-liver-and-tumor-segmentation-of-ct","title":"Automatic Liver and Tumor Segmentation of CT and MRI Volumes using Cascaded Fully Convolutional Neural Networks","arxiv_id":"1702.05970","date":"2017-02-20","proceeding":null,"authors":["Patrick Ferdinand Christ","Florian Ettlinger","Felix Grün","Mohamed Ezzeldin A. Elshaera","Jana Lipkova","Sebastian Schlecht","Freba Ahmaddy","Sunil Tatavarty","Marc Bickel","Patrick Bilic","Markus Rempfler","Felix Hofmann","Melvin D Anastasi","Seyed-Ahmad Ahmadi","Georgios Kaissis","Julian Holch","Wieland Sommer","Rickmer Braren","Volker Heinemann","Bjoern Menze"],"abstract":"Automatic segmentation of the liver and hepatic lesions is an important step\ntowards deriving quantitative biomarkers for accurate clinical diagnosis and\ncomputer-aided decision support systems. This paper presents a method to\nautomatically segment liver and lesions in CT and MRI abdomen images using\ncascaded fully convolutional neural networks (CFCNs) enabling the segmentation\nof a large-scale medical trial or quantitative image analysis. We train and\ncascade two FCNs for a combined segmentation of the liver and its lesions. In\nthe first step, we train a FCN to segment the liver as ROI input for a second\nFCN. The second FCN solely segments lesions within the predicted liver ROIs of\nstep 1. CFCN models were trained on an abdominal CT dataset comprising 100\nhepatic tumor volumes. Validations on further datasets show that CFCN-based\nsemantic liver and lesion segmentation achieves Dice scores over 94% for liver\nwith computation times below 100s per volume. We further experimentally\ndemonstrate the robustness of the proposed method on an 38 MRI liver tumor\nvolumes and the public 3DIRCAD dataset.","url_abs":"http://arxiv.org/abs/1702.05970v2","url_pdf":"http://arxiv.org/pdf/1702.05970v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"automatic-liver-and-tumor-segmentation-of-ct","repo_url":"https://github.com/IBBM/Cascaded-FCN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"NOASSERTION"}},{"paper_slug":"automatic-liver-and-tumor-segmentation-of-ct","repo_url":"https://github.com/FelixGruen/tensorflow-u-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"automatic-liver-and-tumor-segmentation-of-ct","repo_url":"https://github.com/klin059/lits","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"automatic-liver-and-tumor-segmentation-of-ct","repo_url":"https://github.com/soribadiaby/Deep-Learning-liver-segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"automatic-liver-and-tumor-segmentation","task_name":"Automatic Liver And Tumor Segmentation"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"fcn","method_name":"FCN"},{"method_slug":"max-pooling","method_name":"Max Pooling"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1702.05970","atlas_url":"https://app.syntology.ai/?focus=1702.05970","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}