{"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-convolutional-neural-networks-for-7","title":"Deep convolutional neural networks for segmenting 3D in vivo multiphoton images of vasculature in Alzheimer disease mouse models","arxiv_id":"1801.00880","date":"2018-01-03","proceeding":null,"authors":["Mohammad Haft-Javaherian","Linjing Fang","Victorine Muse","Chris B. Schaffer","Nozomi Nishimura","Mert R. Sabuncu"],"abstract":"The health and function of tissue rely on its vasculature network to provide\nreliable blood perfusion. Volumetric imaging approaches, such as multiphoton\nmicroscopy, are able to generate detailed 3D images of blood vessels that could\ncontribute to our understanding of the role of vascular structure in normal\nphysiology and in disease mechanisms. The segmentation of vessels, a core image\nanalysis problem, is a bottleneck that has prevented the systematic comparison\nof 3D vascular architecture across experimental populations. We explored the\nuse of convolutional neural networks to segment 3D vessels within volumetric in\nvivo images acquired by multiphoton microscopy. We evaluated different network\narchitectures and machine learning techniques in the context of this\nsegmentation problem. We show that our optimized convolutional neural network\narchitecture, which we call DeepVess, yielded a segmentation accuracy that was\nbetter than both the current state-of-the-art and a trained human annotator,\nwhile also being orders of magnitude faster. To explore the effects of aging\nand Alzheimer's disease on capillaries, we applied DeepVess to 3D images of\ncortical blood vessels in young and old mouse models of Alzheimer's disease and\nwild type littermates. We found little difference in the distribution of\ncapillary diameter or tortuosity between these groups, but did note a decrease\nin the number of longer capillary segments ($>75\\mu m$) in aged animals as\ncompared to young, in both wild type and Alzheimer's disease mouse models.","url_abs":"http://arxiv.org/abs/1801.00880v4","url_pdf":"http://arxiv.org/pdf/1801.00880v4.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-convolutional-neural-networks-for-7","repo_url":"https://github.com/mhaft/DeepVess","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"deep-convolutional-neural-networks-for-7","repo_url":"https://github.com/cornellneuronex/DeepVess-1","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"segmentation","task_name":"Segmentation"}],"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}