Peter Somers, Mario Deutschmann, Simon Holdenried-Krafft, Samuel Tovey, Johannes Schule, Carina Veil, Valese Aslani, Oliver Sawodny, Hendrik P A Lensch, Cristina Tarin
{"title":"用于单眼深度估计的增强型合成膀胱镜环境","authors":"Peter Somers, Mario Deutschmann, Simon Holdenried-Krafft, Samuel Tovey, Johannes Schule, Carina Veil, Valese Aslani, Oliver Sawodny, Hendrik P A Lensch, Cristina Tarin","doi":"10.1109/EMBC40787.2023.10340303","DOIUrl":null,"url":null,"abstract":"<p><p>As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.</p>","PeriodicalId":72237,"journal":{"name":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.\",\"authors\":\"Peter Somers, Mario Deutschmann, Simon Holdenried-Krafft, Samuel Tovey, Johannes Schule, Carina Veil, Valese Aslani, Oliver Sawodny, Hendrik P A Lensch, Cristina Tarin\",\"doi\":\"10.1109/EMBC40787.2023.10340303\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.</p>\",\"PeriodicalId\":72237,\"journal\":{\"name\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-07-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EMBC40787.2023.10340303\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EMBC40787.2023.10340303","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An Enhanced Synthetic Cystoscopic Environment for Use in Monocular Depth Estimation.
As technology advances and sensing devices improve, it is becoming more and more pertinent to ensure accurate positioning of these devices, especially within the human body. This task remains particularly difficult during manual, minimally invasive surgeries such as cystoscopies where only a monocular, endoscopic camera image is available and driven by hand. Tracking relies on optical localization methods, however, existing classical options do not function well in such a dynamic, non-rigid environment. This work builds on recent works using neural networks to learn a supervised depth estimation from synthetically generated images and, in a second training step, use adversarial training to then apply the network on real images. The improvements made to a synthetic cystoscopic environment are done in such a way to reduce the domain gap between the synthetic images and the real ones. Training with the proposed enhanced environment shows distinct improvements over previously published work when applied to real test images.