{"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/quantitative-analysis-of-patch-based-fully","title":"Quantitative analysis of patch-based fully convolutional neural networks for tissue segmentation on brain magnetic resonance imaging","arxiv_id":"1801.06457","date":"2018-01-19","proceeding":null,"authors":["Jose Bernal","Kaisar Kushibar","Mariano Cabezas","Sergi Valverde","Arnau Oliver","Xavier Lladó"],"abstract":"Accurate brain tissue segmentation in Magnetic Resonance Imaging (MRI) has\nattracted the attention of medical doctors and researchers since variations in\ntissue volume help in diagnosing and monitoring neurological diseases. Several\nproposals have been designed throughout the years comprising conventional\nmachine learning strategies as well as convolutional neural networks (CNN)\napproaches. In particular, in this paper, we analyse a sub-group of deep\nlearning methods producing dense predictions. This branch, referred in the\nliterature as Fully CNN (FCNN), is of interest as these architectures can\nprocess an input volume in less time than CNNs and local spatial dependencies\nmay be encoded since several voxels are classified at once. Our study focuses\non understanding architectural strengths and weaknesses of literature-like\napproaches. Hence, we implement eight FCNN architectures inspired by robust\nstate-of-the-art methods on brain segmentation related tasks. We evaluate them\nusing the IBSR18, MICCAI2012 and iSeg2017 datasets as they contain infant and\nadult data and exhibit varied voxel spacing, image quality, number of scans and\navailable imaging modalities. The discussion is driven in three directions:\ncomparison between 2D and 3D approaches, the importance of multiple modalities\nand overlapping as a sampling strategy for training and testing models. To\nencourage other researchers to explore the evaluation framework, a public\nversion is accessible to download from our research website.","url_abs":"http://arxiv.org/abs/1801.06457v2","url_pdf":"http://arxiv.org/pdf/1801.06457v2.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":"quantitative-analysis-of-patch-based-fully","repo_url":"https://github.com/NIC-VICOROB/tissue_segmentation_comparison","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null}],"tasks":[{"task_slug":"brain-segmentation","task_name":"Brain 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}