{"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/lung-nodule-segmentation-via-level-set","title":"Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation","arxiv_id":"1910.03191","date":"2019-10-08","proceeding":null,"authors":["Matthew C Hancock","Jerry F Magnan"],"abstract":"Purpose: Lung nodule segmentation, i.e., the algorithmic delineation of the lung nodule surface, is a fundamental component of computational nodule analysis pipelines. We propose a new method for segmentation that is a machine learning based extension of current approaches, using labeled image examples to improve its accuracy. Approach: We introduce an extension of the standard level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than a priori designed. Instead, the method employs a set of features to learn a velocity function that guides the level set evolution from initialization. Results: We apply the method to image volumes of lung nodules from CT scans in the publicly available LIDC dataset, obtaining an average intersection over union score of 0.7185($\\pm$0.1114), which is competitive with other methods. We analyze segmentation performance by anatomical and appearance-based categories of the nodules, finding that the method performs better for isolated nodules with well-defined margins. We find that the segmentation performance for nodules in more complex surroundings and having more complex CT appearance is improved with the addition of combined global-local features. Conclusions: The level set machine learning segmentation approach proposed herein is competitive with current methods. It provides accurate lung nodule segmentation results in a variety of anatomical contexts.","url_abs":"https://arxiv.org/abs/1910.03191v3","url_pdf":"https://arxiv.org/pdf/1910.03191v3.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":"lung-nodule-segmentation-via-level-set","repo_url":"https://github.com/notmatthancock/level-set-machine-learning","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"lung-nodule-segmentation","task_name":"Lung Nodule Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}