{"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/fusionnet-a-deep-fully-residual-convolutional","title":"FusionNet: A deep fully residual convolutional neural network for image segmentation in connectomics","arxiv_id":"1612.05360","date":"2016-12-16","proceeding":null,"authors":["Tran Minh Quan","David G. C. Hildebrand","Won-Ki Jeong"],"abstract":"Electron microscopic connectomics is an ambitious research direction with the\ngoal of studying comprehensive brain connectivity maps by using\nhigh-throughput, nano-scale microscopy. One of the main challenges in\nconnectomics research is developing scalable image analysis algorithms that\nrequire minimal user intervention. Recently, deep learning has drawn much\nattention in computer vision because of its exceptional performance in image\nclassification tasks. For this reason, its application to connectomic analyses\nholds great promise, as well. In this paper, we introduce a novel deep neural\nnetwork architecture, FusionNet, for the automatic segmentation of neuronal\nstructures in connectomics data. FusionNet leverages the latest advances in\nmachine learning, such as semantic segmentation and residual neural networks,\nwith the novel introduction of summation-based skip connections to allow a much\ndeeper network architecture for a more accurate segmentation. We demonstrate\nthe performance of the proposed method by comparing it with state-of-the-art\nelectron microscopy (EM) segmentation methods from the ISBI EM segmentation\nchallenge. We also show the segmentation results on two different tasks\nincluding cell membrane and cell body segmentation and a statistical analysis\nof cell morphology.","url_abs":"http://arxiv.org/abs/1612.05360v2","url_pdf":"http://arxiv.org/pdf/1612.05360v2.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":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/GunhoChoi/FusionNet-Pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/Jeongseungwoo/Fusion-net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/MiRA-lab-dev/SynRec","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/aparecidovieira/Keras_FusionNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/chenhong-zhou/OM-Net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"fusionnet-a-deep-fully-residual-convolutional","repo_url":"https://github.com/umd-fire-coml/2020-Object-Detection-In-Aerial-Images","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"brain-image-segmentation","task_name":"Brain Image Segmentation"},{"task_slug":"image-classification","task_name":"Image Classification"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"image-classification","task_name":"image-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1612.05360","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}