{"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/deep-neural-networks-for-anatomical-brain","title":"Deep Neural Networks for Anatomical Brain Segmentation","arxiv_id":"1502.02445","date":"2015-02-09","proceeding":null,"authors":["Alexandre de Brebisson","Giovanni Montana"],"abstract":"We present a novel approach to automatically segment magnetic resonance (MR)\nimages of the human brain into anatomical regions. Our methodology is based on\na deep artificial neural network that assigns each voxel in an MR image of the\nbrain to its corresponding anatomical region. The inputs of the network capture\ninformation at different scales around the voxel of interest: 3D and orthogonal\n2D intensity patches capture the local spatial context while large, compressed\n2D orthogonal patches and distances to the regional centroids enforce global\nspatial consistency. Contrary to commonly used segmentation methods, our\ntechnique does not require any non-linear registration of the MR images. To\nbenchmark our model, we used the dataset provided for the MICCAI 2012 challenge\non multi-atlas labelling, which consists of 35 manually segmented MR images of\nthe brain. We obtained competitive results (mean dice coefficient 0.725, error\nrate 0.163) showing the potential of our approach. To our knowledge, our\ntechnique is the first to tackle the anatomical segmentation of the whole brain\nusing deep neural networks.","url_abs":"http://arxiv.org/abs/1502.02445v2","url_pdf":"http://arxiv.org/pdf/1502.02445v2.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":"deep-neural-networks-for-anatomical-brain","repo_url":"https://github.com/ashishpatel26/BrainMRI-Segmentation-Keras","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"deep-neural-networks-for-anatomical-brain","repo_url":"https://github.com/bclwan/MRI_Brain_Segmentation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}}],"tasks":[{"task_slug":"brain-segmentation","task_name":"Brain Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1502.02445","atlas_url":"https://app.syntology.ai/?focus=1502.02445","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}