{"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/neuronet-fast-and-robust-reproduction-of","title":"NeuroNet: Fast and Robust Reproduction of Multiple Brain Image Segmentation Pipelines","arxiv_id":"1806.04224","date":"2018-06-11","proceeding":null,"authors":["Martin Rajchl","Nick Pawlowski","Daniel Rueckert","Paul M. Matthews","Ben Glocker"],"abstract":"NeuroNet is a deep convolutional neural network mimicking multiple popular\nand state-of-the-art brain segmentation tools including FSL, SPM, and MALPEM.\nThe network is trained on 5,000 T1-weighted brain MRI scans from the UK Biobank\nImaging Study that have been automatically segmented into brain tissue and\ncortical and sub-cortical structures using the standard neuroimaging pipelines.\nTraining a single model from these complementary and partially overlapping\nlabel maps yields a new powerful \"all-in-one\", multi-output segmentation tool.\nThe processing time for a single subject is reduced by an order of magnitude\ncompared to running each individual software package. We demonstrate very good\nreproducibility of the original outputs while increasing robustness to\nvariations in the input data. We believe NeuroNet could be an important tool in\nlarge-scale population imaging studies and serve as a new standard in\nneuroscience by reducing the risk of introducing bias when choosing a specific\nsoftware package.","url_abs":"http://arxiv.org/abs/1806.04224v1","url_pdf":"http://arxiv.org/pdf/1806.04224v1.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":"neuronet-fast-and-robust-reproduction-of","repo_url":"https://github.com/DLTK/models","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"brain-image-segmentation","task_name":"Brain Image Segmentation"},{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1806.04224","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1806.04224"}},"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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