{"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/domain-adaptive-segmentation-in-volume","title":"Domain Adaptive Segmentation in Volume Electron Microscopy Imaging","arxiv_id":"1810.09734","date":"2018-10-23","proceeding":null,"authors":["Joris Roels","Julian Hennies","Yvan Saeys","Wilfried Philips","Anna Kreshuk"],"abstract":"In the last years, automated segmentation has become a necessary tool for\nvolume electron microscopy (EM) imaging. So far, the best performing techniques\nhave been largely based on fully supervised encoder-decoder CNNs, requiring a\nsubstantial amount of annotated images. Domain Adaptation (DA) aims to\nalleviate the annotation burden by 'adapting' the networks trained on existing\ngroundtruth data (source domain) to work on a different (target) domain with as\nlittle additional annotation as possible. Most DA research is focused on the\nclassification task, whereas volume EM segmentation remains rather unexplored.\nIn this work, we extend recently proposed classification DA techniques to an\nencoder-decoder layout and propose a novel method that adds a reconstruction\ndecoder to the classical encoder-decoder segmentation in order to align source\nand target encoder features. The method has been validated on the task of\nsegmenting mitochondria in EM volumes. We have performed DA from brain EM\nimages to HeLa cells and from isotropic FIB/SEM volumes to anisotropic TEM\nvolumes. In all cases, the proposed method has outperformed the extended\nclassification DA techniques and the finetuning baseline. An implementation of\nour work can be found on\nhttps://github.com/JorisRoels/domain-adaptive-segmentation.","url_abs":"http://arxiv.org/abs/1810.09734v2","url_pdf":"http://arxiv.org/pdf/1810.09734v2.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":"domain-adaptive-segmentation-in-volume","repo_url":"https://github.com/JorisRoels/domain-adaptive-segmentation","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"segmentation","task_name":"Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1810.09734","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}