{"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/recursive-training-of-2d-3d-convolutional","title":"Recursive Training of 2D-3D Convolutional Networks for Neuronal Boundary Detection","arxiv_id":"1508.04843","date":"2015-08-20","proceeding":"NeurIPS 2015","authors":["Kisuk Lee","Aleksandar Zlateski","Ashwin Vishwanathan","H. Sebastian Seung"],"abstract":"Efforts to automate the reconstruction of neural circuits from 3D electron\nmicroscopic (EM) brain images are critical for the field of connectomics. An\nimportant computation for reconstruction is the detection of neuronal\nboundaries. Images acquired by serial section EM, a leading 3D EM technique,\nare highly anisotropic, with inferior quality along the third dimension. For\nsuch images, the 2D max-pooling convolutional network has set the standard for\nperformance at boundary detection. Here we achieve a substantial gain in\naccuracy through three innovations. Following the trend towards deeper networks\nfor object recognition, we use a much deeper network than previously employed\nfor boundary detection. Second, we incorporate 3D as well as 2D filters, to\nenable computations that use 3D context. Finally, we adopt a recursively\ntrained architecture in which a first network generates a preliminary boundary\nmap that is provided as input along with the original image to a second network\nthat generates a final boundary map. Backpropagation training is accelerated by\nZNN, a new implementation of 3D convolutional networks that uses multicore CPU\nparallelism for speed. Our hybrid 2D-3D architecture could be more generally\napplicable to other types of anisotropic 3D images, including video, and our\nrecursive framework for any image labeling problem.","url_abs":"http://arxiv.org/abs/1508.04843v1","url_pdf":"http://arxiv.org/pdf/1508.04843v1.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":"recursive-training-of-2d-3d-convolutional","repo_url":"https://github.com/seung-lab/znn-release","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"recursive-training-of-2d-3d-convolutional","repo_url":"https://github.com/petteriTeikari/vesselNN","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-architecture","task_name":"3D Architecture"},{"task_slug":"boundary-detection","task_name":"Boundary Detection"},{"task_slug":null,"task_name":"CPU"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}