{"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/detection-aided-liver-lesion-segmentation","title":"Detection-aided liver lesion segmentation using deep learning","arxiv_id":"1711.11069","date":"2017-11-29","proceeding":null,"authors":["Miriam Bellver","Kevis-Kokitsi Maninis","Jordi Pont-Tuset","Xavier Giro-i-Nieto","Jordi Torres","Luc van Gool"],"abstract":"A fully automatic technique for segmenting the liver and localizing its\nunhealthy tissues is a convenient tool in order to diagnose hepatic diseases\nand assess the response to the according treatments. In this work we propose a\nmethod to segment the liver and its lesions from Computed Tomography (CT) scans\nusing Convolutional Neural Networks (CNNs), that have proven good results in a\nvariety of computer vision tasks, including medical imaging. The network that\nsegments the lesions consists of a cascaded architecture, which first focuses\non the region of the liver in order to segment the lesions on it. Moreover, we\ntrain a detector to localize the lesions, and mask the results of the\nsegmentation network with the positive detections. The segmentation\narchitecture is based on DRIU, a Fully Convolutional Network (FCN) with side\noutputs that work on feature maps of different resolutions, to finally benefit\nfrom the multi-scale information learned by different stages of the network.\nThe main contribution of this work is the use of a detector to localize the\nlesions, which we show to be beneficial to remove false positives triggered by\nthe segmentation network. Source code and models are available at\nhttps://imatge-upc.github.io/liverseg-2017-nipsws/ .","url_abs":"http://arxiv.org/abs/1711.11069v1","url_pdf":"http://arxiv.org/pdf/1711.11069v1.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":"detection-aided-liver-lesion-segmentation","repo_url":"https://github.com/imatge-upc/liverseg-2017-nipsws","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"detection-aided-liver-lesion-segmentation","repo_url":"https://github.com/Sempronius/LITS_test","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"computed-tomography-ct","task_name":"Computed Tomography (CT)"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"lesion-segmentation","task_name":"Lesion Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}