{"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/micro-net-a-unified-model-for-segmentation-of","title":"Micro-Net: A unified model for segmentation of various objects in microscopy images","arxiv_id":"1804.08145","date":"2018-04-22","proceeding":null,"authors":["Shan E Ahmed Raza","Linda Cheung","Muhammad Shaban","Simon Graham","David Epstein","Stella Pelengaris","Michael Khan","Nasir M. Rajpoot"],"abstract":"Object segmentation and structure localization are important steps in\nautomated image analysis pipelines for microscopy images. We present a\nconvolution neural network (CNN) based deep learning architecture for\nsegmentation of objects in microscopy images. The proposed network can be used\nto segment cells, nuclei and glands in fluorescence microscopy and histology\nimages after slight tuning of input parameters. The network trains at multiple\nresolutions of the input image, connects the intermediate layers for better\nlocalization and context and generates the output using multi-resolution\ndeconvolution filters. The extra convolutional layers which bypass the\nmax-pooling operation allow the network to train for variable input intensities\nand object size and make it robust to noisy data. We compare our results on\npublicly available data sets and show that the proposed network outperforms\nrecent deep learning algorithms.","url_abs":"http://arxiv.org/abs/1804.08145v2","url_pdf":"http://arxiv.org/pdf/1804.08145v2.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":[],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"},{"task_slug":"multi-tissue-nucleus-segmentation","task_name":"Multi-tissue Nucleus Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/multi-tissue-nucleus-segmentation-on-kumar","task":"Multi-tissue Nucleus Segmentation","dataset":"Kumar","model":"Micro-Net (e)","rank_in_archive_order":13,"of":18,"metrics":{"Dice":"0.797","Hausdorff Distance (mm)":"51.9"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1804.08145","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}