{"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/dilated-deeply-supervised-networks-for","title":"Dilated deeply supervised networks for hippocampus segmentation in MRI","arxiv_id":"1903.09097","date":"2019-03-20","proceeding":null,"authors":["Lukas Folle","Sulaiman Vesal","Nishant Ravikumar","Andreas Maier"],"abstract":"Tissue loss in the hippocampi has been heavily correlated with the\nprogression of Alzheimer's Disease (AD). The shape and structure of the\nhippocampus are important factors in terms of early AD diagnosis and prognosis\nby clinicians. However, manual segmentation of such subcortical structures in\nMR studies is a challenging and subjective task. In this paper, we investigate\nvariants of the well known 3D U-Net, a type of convolution neural network (CNN)\nfor semantic segmentation tasks. We propose an alternative form of the 3D\nU-Net, which uses dilated convolutions and deep supervision to incorporate\nmulti-scale information into the model. The proposed method is evaluated on the\ntask of hippocampus head and body segmentation in an MRI dataset, provided as\npart of the MICCAI 2018 segmentation decathlon challenge. The experimental\nresults show that our approach outperforms other conventional methods in terms\nof different segmentation accuracy metrics.","url_abs":"http://arxiv.org/abs/1903.09097v1","url_pdf":"http://arxiv.org/pdf/1903.09097v1.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":"dilated-deeply-supervised-networks-for","repo_url":"https://github.com/satyakees/FaultNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":null,"task_name":"Hippocampus"},{"task_slug":"prognosis","task_name":"Prognosis"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}