{"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/advanced-deep-networks-for-3d-mitochondria","title":"Advanced Deep Networks for 3D Mitochondria Instance Segmentation","arxiv_id":"2104.07961","date":"2021-04-16","proceeding":null,"authors":["Mingxing Li","Chang Chen","Xiaoyu Liu","Wei Huang","Yueyi Zhang","Zhiwei Xiong"],"abstract":"Mitochondria instance segmentation from electron microscopy (EM) images has seen notable progress since the introduction of deep learning methods. In this paper, we propose two advanced deep networks, named Res-UNet-R and Res-UNet-H, for 3D mitochondria instance segmentation from Rat and Human samples. Specifically, we design a simple yet effective anisotropic convolution block and deploy a multi-scale training strategy, which together boost the segmentation performance. Moreover, we enhance the generalizability of the trained models on the test set by adding a denoising operation as pre-processing. In the Large-scale 3D Mitochondria Instance Segmentation Challenge at ISBI 2021, our method ranks the 1st place. Code is available at https://github.com/Limingxing00/MitoEM2021-Challenge.","url_abs":"https://arxiv.org/abs/2104.07961v4","url_pdf":"https://arxiv.org/pdf/2104.07961v4.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":"advanced-deep-networks-for-3d-mitochondria","repo_url":"https://github.com/Limingxing00/MitoEM2021-Challenge","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-instance-segmentation-1","task_name":"3D Instance Segmentation"},{"task_slug":"denoising","task_name":"Denoising"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-instance-segmentation-on-mitoem","task":"3D Instance Segmentation","dataset":"MitoEM","model":"Res-UNet-R/H","rank_in_archive_order":1,"of":2,"metrics":{"AP75-H-Test":"0.829 ","AP75-H-Val":"0.828","AP75-R-Test":"0.851","AP75-R-Val":"0.917"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2104.07961","atlas_url":"https://app.syntology.ai/?focus=2104.07961","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}