{"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/a-fast-segmentation-free-fully-automated","title":"A Fast Segmentation-free Fully Automated Approach to White Matter Injury Detection in Preterm Infants","arxiv_id":"1807.06604","date":"2018-07-17","proceeding":null,"authors":["Subhayan Mukherjee","Irene Cheng","Steven Miller","Jessie Guo","Vann Chau","Anup Basu"],"abstract":"White Matter Injury (WMI) is the most prevalent brain injury in the preterm\nneonate leading to developmental deficits. However, detecting WMI in Magnetic\nResonance (MR) images of preterm neonate brains using traditional WM\nsegmentation-based methods is difficult mainly due to lack of reliable preterm\nneonate brain atlases to guide segmentation. Hence, we propose a\nsegmentation-free, fast, unsupervised, atlas-free WMI detection method. We\ndetect the ventricles as blobs using a fast linear Maximally Stable Extremal\nRegions algorithm. A reference contour equidistant from the blobs and the\nbrain-background boundary is used to identify tissue adjacent to the blobs.\nAssuming normal distribution of the gray-value intensity of this tissue, the\noutlier intensities in the entire brain region are identified as potential WMI\ncandidates. Thereafter, false positives are discriminated using appropriate\nheuristics. Experiments using an expert-annotated dataset show that the\nproposed method runs 20 times faster than our earlier work which relied on\ntime-consuming segmentation of the WM region, without compromising WMI\ndetection accuracy.","url_abs":"http://arxiv.org/abs/1807.06604v1","url_pdf":"http://arxiv.org/pdf/1807.06604v1.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":"a-fast-segmentation-free-fully-automated","repo_url":"https://github.com/subhayanmukherjee/fastwmi","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}