{"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/conditional-random-fields-as-recurrent-neural","title":"Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation","arxiv_id":"1807.07464","date":"2018-07-19","proceeding":null,"authors":["Miguel Monteiro","Mário A. T. Figueiredo","Arlindo L. Oliveira"],"abstract":"The Conditional Random Field as a Recurrent Neural Network layer is a\nrecently proposed algorithm meant to be placed on top of an existing\nFully-Convolutional Neural Network to improve the quality of semantic\nsegmentation. In this paper, we test whether this algorithm, which was shown to\nimprove semantic segmentation for 2D RGB images, is able to improve\nsegmentation quality for 3D multi-modal medical images. We developed an\nimplementation of the algorithm which works for any number of spatial\ndimensions, input/output image channels, and reference image channels. As far\nas we know this is the first publicly available implementation of this sort. We\ntested the algorithm with two distinct 3D medical imaging datasets, we\nconcluded that the performance differences observed were not statistically\nsignificant. Finally, in the discussion section of the paper, we go into the\nreasons as to why this technique transfers poorly from natural images to\nmedical images.","url_abs":"http://arxiv.org/abs/1807.07464v1","url_pdf":"http://arxiv.org/pdf/1807.07464v1.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":"conditional-random-fields-as-recurrent-neural","repo_url":"https://github.com/MiguelMonteiro/CRFasRNNLayer","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"conditional-random-fields-as-recurrent-neural","repo_url":"https://github.com/MiguelMonteiro/permutohedral_lattice","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"3d-medical-imaging-segmentation","task_name":"3D Medical Imaging Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"volumetric-medical-image-segmentation","task_name":"Volumetric Medical Image Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/volumetric-medical-image-segmentation-on","task":"Volumetric Medical Image Segmentation","dataset":"PROMISE 2012","model":"Fully-connected CRF","rank_in_archive_order":2,"of":2,"metrics":{"Dice Score":"0.780"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.07464","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.07464"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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