Zhengyu Wang;Ziqian Li;Xiang Yu;Zirui Jia;Xinzhou Xu;Björn W. Schuller
{"title":"Cross-Scene Semantic Segmentation for Medical Surgical Instruments Using Structural Similarity-Based Partial Activation Networks","authors":"Zhengyu Wang;Ziqian Li;Xiang Yu;Zirui Jia;Xinzhou Xu;Björn W. Schuller","doi":"10.1109/TMRB.2024.3359303","DOIUrl":null,"url":null,"abstract":"Robot-assisted minimally invasive surgery requires accurate segmentation for surgical instruments in order to guide surgical robots on tracking the target instruments. Nevertheless, it is difficult to perform surgical-instrument semantic segmentation in unknown scenes with extremely insufficient intra-scene surgical data, despite of the attempts for general semantic segmentation tasks. To address this issue, we propose a cross-scene semantic segmentation approach for medical surgical instruments using structural similarity based partial activation networks in this paper. The proposed approach includes a main branch for multi-level feature extraction, a segmentation head global consistency, and a structural similarity based loss function to provide high-level information acquisition, which improves the generalisation performance for the cross-scene segmentation task. Then, the experimental results in cross-scene surgical-instrument semantic segmentation cases show the effectiveness of the proposed approach compared with state-of-the-art semantic segmentation ones, using the newly established endoscopic simulation dataset.","PeriodicalId":73318,"journal":{"name":"IEEE transactions on medical robotics and bionics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on medical robotics and bionics","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10415635/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Robot-assisted minimally invasive surgery requires accurate segmentation for surgical instruments in order to guide surgical robots on tracking the target instruments. Nevertheless, it is difficult to perform surgical-instrument semantic segmentation in unknown scenes with extremely insufficient intra-scene surgical data, despite of the attempts for general semantic segmentation tasks. To address this issue, we propose a cross-scene semantic segmentation approach for medical surgical instruments using structural similarity based partial activation networks in this paper. The proposed approach includes a main branch for multi-level feature extraction, a segmentation head global consistency, and a structural similarity based loss function to provide high-level information acquisition, which improves the generalisation performance for the cross-scene segmentation task. Then, the experimental results in cross-scene surgical-instrument semantic segmentation cases show the effectiveness of the proposed approach compared with state-of-the-art semantic segmentation ones, using the newly established endoscopic simulation dataset.