{"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/hierarchical-3d-fully-convolutional-networks","title":"Hierarchical 3D fully convolutional networks for multi-organ segmentation","arxiv_id":"1704.06382","date":"2017-04-21","proceeding":null,"authors":["Holger R. Roth","Hirohisa ODA","Yuichiro Hayashi","Masahiro Oda","Natsuki Shimizu","Michitaka Fujiwara","Kazunari Misawa","Kensaku MORI"],"abstract":"Recent advances in 3D fully convolutional networks (FCN) have made it\nfeasible to produce dense voxel-wise predictions of full volumetric images. In\nthis work, we show that a multi-class 3D FCN trained on manually labeled CT\nscans of seven abdominal structures (artery, vein, liver, spleen, stomach,\ngallbladder, and pancreas) can achieve competitive segmentation results, while\navoiding the need for handcrafting features or training organ-specific models.\nTo this end, we propose a two-stage, coarse-to-fine approach that trains an FCN\nmodel to roughly delineate the organs of interest in the first stage (seeing\n$\\sim$40% of the voxels within a simple, automatically generated binary mask of\nthe patient's body). We then use these predictions of the first-stage FCN to\ndefine a candidate region that will be used to train a second FCN. This step\nreduces the number of voxels the FCN has to classify to $\\sim$10% while\nmaintaining a recall high of $>$99%. This second-stage FCN can now focus on\nmore detailed segmentation of the organs. We respectively utilize training and\nvalidation sets consisting of 281 and 50 clinical CT images. Our hierarchical\napproach provides an improved Dice score of 7.5 percentage points per organ on\naverage in our validation set. We furthermore test our models on a completely\nunseen data collection acquired at a different hospital that includes 150 CT\nscans with three anatomical labels (liver, spleen, and pancreas). In such\nchallenging organs as the pancreas, our hierarchical approach improves the mean\nDice score from 68.5 to 82.2%, achieving the highest reported average score on\nthis dataset.","url_abs":"http://arxiv.org/abs/1704.06382v1","url_pdf":"http://arxiv.org/pdf/1704.06382v1.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":"hierarchical-3d-fully-convolutional-networks","repo_url":"https://github.com/holgerroth/3Dunet_abdomen_cascade","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"organ-segmentation","task_name":"Organ 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":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}