{"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/an-automatic-nuclei-image-segmentation-based","title":"An Automatic Nuclei Image Segmentation Based on Multi-Scale Split-Attention U-Net","arxiv_id":null,"date":"2021-07-20","proceeding":"MICCAI Workshop COMPAY 2021 9","authors":["Qing Xu","Wenting Duan"],"abstract":"Nuclei segmentation is an important step in the task of medical image analysis. Nowadays,\ndeep learning techniques based on Convolutional Neural Networks (CNNs) have become\nprevalent methods in nuclei segmentation. In this paper, we propose a network called\nMulti-scale Split-Attention U-Net (MSAU-Net) for further improving the performance of\ncell segmentation. MSAU-Net is based on U-Net architecture and the original blocks used to\ndown-sampling and up-sampling paths are replaced with Multi-scale Split-Attention blocks\nfor capturing independent semantic information of nuclei images. A public microscopy image\ndataset from 2018 Data Science Bowl grand challenge is selected to train and evaluate\nMSAU-Net. By running trained models on the test set, our model reaches average Intersection\nover Union (IoU) of 0.851, which is better than other prominent models, especially\n4.8 percent higher than the original U-Net. For other evaluation metrics including accuracy,\nprecision, recall and F1-score, MSAU-Net shows better performance in the most of\nindicators. The outstanding result reveals that our proposed model presents a promising\nnuclei segmentation method for the microscopy image analysis.","url_abs":"https://openreview.net/forum?id=y67TdZrydHp","url_pdf":"https://openreview.net/pdf?id=y67TdZrydHp","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":"an-automatic-nuclei-image-segmentation-based","repo_url":"https://github.com/xq141839/MSAU-Net","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"cell-segmentation","task_name":"Cell Segmentation"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"medical-image-analysis","task_name":"Medical Image Analysis"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}