{"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/u-netplus-a-modified-encoder-decoder-u-net","title":"U-NetPlus: A Modified Encoder-Decoder U-Net Architecture for Semantic and Instance Segmentation of Surgical Instrument","arxiv_id":"1902.08994","date":"2019-02-24","proceeding":null,"authors":["S. M. Kamrul Hasan","Cristian A. Linte"],"abstract":"Conventional therapy approaches limit surgeons' dexterity control due to\nlimited field-of-view. With the advent of robot-assisted surgery, there has\nbeen a paradigm shift in medical technology for minimally invasive surgery.\nHowever, it is very challenging to track the position of the surgical\ninstruments in a surgical scene, and accurate detection & identification of\nsurgical tools is paramount. Deep learning-based semantic segmentation in\nframes of surgery videos has the potential to facilitate this task. In this\nwork, we modify the U-Net architecture named U-NetPlus, by introducing a\npre-trained encoder and re-design the decoder part, by replacing the transposed\nconvolution operation with an upsampling operation based on nearest-neighbor\n(NN) interpolation. To further improve performance, we also employ a very fast\nand flexible data augmentation technique. We trained the framework on 8 x 225\nframe sequences of robotic surgical videos, available through the MICCAI 2017\nEndoVis Challenge dataset and tested it on 8 x 75 frame and 2 x 300 frame\nvideos. Using our U-NetPlus architecture, we report a 90.20% DICE for binary\nsegmentation, 76.26% DICE for instrument part segmentation, and 46.07% for\ninstrument type (i.e., all instruments) segmentation, outperforming the results\nof previous techniques implemented and tested on these data.","url_abs":"http://arxiv.org/abs/1902.08994v1","url_pdf":"http://arxiv.org/pdf/1902.08994v1.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":"u-netplus-a-modified-encoder-decoder-u-net","repo_url":"https://github.com/SMKamrulHasan/UNetPlus","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"decoder","task_name":"Decoder"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"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}