{"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/deep-learning-convolutional-networks-for","title":"Deep Learning Convolutional Networks for Multiphoton Microscopy Vasculature Segmentation","arxiv_id":"1606.02382","date":"2016-06-08","proceeding":null,"authors":["Petteri Teikari","Marc Santos","Charissa Poon","Kullervo Hynynen"],"abstract":"Recently there has been an increasing trend to use deep learning frameworks\nfor both 2D consumer images and for 3D medical images. However, there has been\nlittle effort to use deep frameworks for volumetric vascular segmentation. We\nwanted to address this by providing a freely available dataset of 12 annotated\ntwo-photon vasculature microscopy stacks. We demonstrated the use of deep\nlearning framework consisting both 2D and 3D convolutional filters (ConvNet).\nOur hybrid 2D-3D architecture produced promising segmentation result. We\nderived the architectures from Lee et al. who used the ZNN framework initially\ndesigned for electron microscope image segmentation. We hope that by sharing\nour volumetric vasculature datasets, we will inspire other researchers to\nexperiment with vasculature dataset and improve the used network architectures.","url_abs":"http://arxiv.org/abs/1606.02382v1","url_pdf":"http://arxiv.org/pdf/1606.02382v1.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":"deep-learning-convolutional-networks-for","repo_url":"https://github.com/petteriTeikari/vesselNN","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"deep-learning-convolutional-networks-for","repo_url":"https://github.com/petteriTeikari/vesselNN_dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"3d-architecture","task_name":"3D Architecture"},{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[{"slug":"2-pm-vessel-dataset","name":"2-PM Vessel Dataset","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1606.02382","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}