Zhengyu Wang;Ziqian Li;Xiang Yu;Zirui Jia;Xinzhou Xu;Björn W. Schuller
{"title":"使用基于结构相似性的部分激活网络对医疗手术器械进行跨场景语义分割","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":"{\"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}","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}
Cross-Scene Semantic Segmentation for Medical Surgical Instruments Using Structural Similarity-Based Partial Activation Networks
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.