{"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/cnn-based-segmentation-of-medical-imaging","title":"CNN-based Segmentation of Medical Imaging Data","arxiv_id":"1701.03056","date":"2017-01-11","proceeding":null,"authors":["Baris Kayalibay","Grady Jensen","Patrick van der Smagt"],"abstract":"Convolutional neural networks have been applied to a wide variety of computer\nvision tasks. Recent advances in semantic segmentation have enabled their\napplication to medical image segmentation. While most CNNs use two-dimensional\nkernels, recent CNN-based publications on medical image segmentation featured\nthree-dimensional kernels, allowing full access to the three-dimensional\nstructure of medical images. Though closely related to semantic segmentation,\nmedical image segmentation includes specific challenges that need to be\naddressed, such as the scarcity of labelled data, the high class imbalance\nfound in the ground truth and the high memory demand of three-dimensional\nimages. In this work, a CNN-based method with three-dimensional filters is\ndemonstrated and applied to hand and brain MRI. Two modifications to an\nexisting CNN architecture are discussed, along with methods on addressing the\naforementioned challenges. While most of the existing literature on medical\nimage segmentation focuses on soft tissue and the major organs, this work is\nvalidated on data both from the central nervous system as well as the bones of\nthe hand.","url_abs":"http://arxiv.org/abs/1701.03056v2","url_pdf":"http://arxiv.org/pdf/1701.03056v2.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":"cnn-based-segmentation-of-medical-imaging","repo_url":"https://github.com/BRML/CNNbasedMedicalSegmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok"}},{"paper_slug":"cnn-based-segmentation-of-medical-imaging","repo_url":"https://github.com/shreyaspadhy/unet-zoo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"brain-tumor-segmentation","task_name":"Brain Tumor Segmentation"},{"task_slug":"image-segmentation","task_name":"Image 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"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/brain-tumor-segmentation-on-brats-2015","task":"Brain Tumor Segmentation","dataset":"BRATS-2015","model":"CNN + 3D filters","rank_in_archive_order":2,"of":4,"metrics":{"Dice Score":"85.0%"},"uses_additional_data":true}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1701.03056","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}