{"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/reducing-the-hausdorff-distance-in-medical","title":"Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks","arxiv_id":"1904.10030","date":"2019-04-22","proceeding":null,"authors":["Davood Karimi","Septimiu E. Salcudean"],"abstract":"The Hausdorff Distance (HD) is widely used in evaluating medical image\nsegmentation methods. However, existing segmentation methods do not attempt to\nreduce HD directly. In this paper, we present novel loss functions for training\nconvolutional neural network (CNN)-based segmentation methods with the goal of\nreducing HD directly. We propose three methods to estimate HD from the\nsegmentation probability map produced by a CNN. One method makes use of the\ndistance transform of the segmentation boundary. Another method is based on\napplying morphological erosion on the difference between the true and estimated\nsegmentation maps. The third method works by applying circular/spherical\nconvolution kernels of different radii on the segmentation probability maps.\nBased on these three methods for estimating HD, we suggest three loss functions\nthat can be used for training to reduce HD. We use these loss functions to\ntrain CNNs for segmentation of the prostate, liver, and pancreas in ultrasound,\nmagnetic resonance, and computed tomography images and compare the results with\ncommonly-used loss functions. Our results show that the proposed loss functions\ncan lead to approximately 18-45 % reduction in HD without degrading other\nsegmentation performance criteria such as the Dice similarity coefficient. The\nproposed loss functions can be used for training medical image segmentation\nmethods in order to reduce the large segmentation errors.","url_abs":"http://arxiv.org/abs/1904.10030v1","url_pdf":"http://arxiv.org/pdf/1904.10030v1.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":"reducing-the-hausdorff-distance-in-medical","repo_url":"https://github.com/PatRyg99/HausdorffLoss","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"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":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1904.10030","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}