{"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/automatic-brain-tumor-segmentation-using-1","title":"Automatic Brain Tumor Segmentation using Cascaded Anisotropic Convolutional Neural Networks","arxiv_id":"1709.00382","date":"2017-09-01","proceeding":null,"authors":["Guotai Wang","Wenqi Li","Sebastien Ourselin","Tom Vercauteren"],"abstract":"A cascade of fully convolutional neural networks is proposed to segment\nmulti-modal Magnetic Resonance (MR) images with brain tumor into background and\nthree hierarchical regions: whole tumor, tumor core and enhancing tumor core.\nThe cascade is designed to decompose the multi-class segmentation problem into\na sequence of three binary segmentation problems according to the subregion\nhierarchy. The whole tumor is segmented in the first step and the bounding box\nof the result is used for the tumor core segmentation in the second step. The\nenhancing tumor core is then segmented based on the bounding box of the tumor\ncore segmentation result. Our networks consist of multiple layers of\nanisotropic and dilated convolution filters, and they are combined with\nmulti-view fusion to reduce false positives. Residual connections and\nmulti-scale predictions are employed in these networks to boost the\nsegmentation performance. Experiments with BraTS 2017 validation set show that\nthe proposed method achieved average Dice scores of 0.7859, 0.9050, 0.8378 for\nenhancing tumor core, whole tumor and tumor core, respectively. The\ncorresponding values for BraTS 2017 testing set were 0.7831, 0.8739, and\n0.7748, respectively.","url_abs":"http://arxiv.org/abs/1709.00382v2","url_pdf":"http://arxiv.org/pdf/1709.00382v2.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":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/Fabienne703/Data","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/charan223/Brain-Tumor-Segmentation-using-Topological-Loss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/charan223/brain_tumor_topology","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/charan223/topology-aware-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/charan223/topology-conscious-networks","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/julianbertini/MSNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}},{"paper_slug":"automatic-brain-tumor-segmentation-using-1","repo_url":"https://github.com/taigw/brats18_docker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"medical-image-segmentation","task_name":"Medical Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"tumor-segmentation","task_name":"Tumor Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dilated-convolution","method_name":"Dilated Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2014","task":"Brain Tumor Segmentation","dataset":"BRATS-2014","model":"Cascaded Anisotropic CNNs","rank_in_archive_order":1,"of":1,"metrics":{"Dice Score":"0.8739"},"uses_additional_data":false},{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2017-val","task":"Brain Tumor Segmentation","dataset":"BRATS-2017 val","model":"Wang et al.","rank_in_archive_order":3,"of":3,"metrics":{"Dice Score":"0.905"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1709.00382","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1709.00382"}},"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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