Placental vessel segmentation and registration in fetoscopy: Literature review and MICCAI FetReg2021 challenge findings.

Autor: Bano S; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, UK. Electronic address: sophia.bano@ucl.ac.uk., Casella A; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., Vasconcelos F; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, UK., Qayyum A; ENIB, UMR CNRS 6285 LabSTICC, 29238, France., Benzinou A; ENIB, UMR CNRS 6285 LabSTICC, 29238, France., Mazher M; Department of Computer Engineering and Mathematics, University Rovira i Virgili, Spain., Meriaudeau F; ImViA Laboratory, University of Bourgogne Franche-Comté, France., Lena C; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., Cintorrino IA; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., De Paolis GR; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., Biagioli J; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., Grechishnikova D; Physics Department, Lomonosov Moscow State University, Russia., Jiao J; Fudan University, China., Bai B; Medical Computer Vision and Robotics Group, Department of Mathematical and Computational Sciences, University of Toronto, Canada., Qiao Y; Shanghai MicroPort MedBot (Group) Co., Ltd, China., Bhattarai B; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, UK., Gaire RR; NepAL Applied Mathematics and Informatics Institute for Research, Nepal., Subedi R; NepAL Applied Mathematics and Informatics Institute for Research, Nepal., Vazquez E; Redev Technology, UK., Płotka S; Sano Center for Computational Medicine, Poland; Quantitative Healthcare Analysis Group, Informatics Institute, University of Amsterdam, Amsterdam, The Netherlands., Lisowska A; Sano Center for Computational Medicine, Poland., Sitek A; Sano Center for Computational Medicine, Poland; Center for Advanced Medical Computing and Simulation, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America., Attilakos G; Fetal Medicine Unit, Elizabeth Garrett Anderson Wing, University College London Hospital, UK; EGA Institute for Women's Health, Faculty of Population Health Sciences, University College London, UK., Wimalasundera R; Fetal Medicine Unit, Elizabeth Garrett Anderson Wing, University College London Hospital, UK; EGA Institute for Women's Health, Faculty of Population Health Sciences, University College London, UK., David AL; Fetal Medicine Unit, Elizabeth Garrett Anderson Wing, University College London Hospital, UK; EGA Institute for Women's Health, Faculty of Population Health Sciences, University College London, UK; Department of Development and Regeneration, University Hospital Leuven, Belgium., Paladini D; Department of Fetal and Perinatal Medicine, Istituto 'Giannina Gaslini', Italy., Deprest J; EGA Institute for Women's Health, Faculty of Population Health Sciences, University College London, UK; Department of Development and Regeneration, University Hospital Leuven, Belgium., De Momi E; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Italy., Mattos LS; Department of Advanced Robotics, Istituto Italiano di Tecnologia, Italy., Moccia S; The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Italy., Stoyanov D; Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) and Department of Computer Science, University College London, UK.
Jazyk: angličtina
Zdroj: Medical image analysis [Med Image Anal] 2024 Feb; Vol. 92, pp. 103066. Date of Electronic Publication: 2023 Dec 20.
DOI: 10.1016/j.media.2023.103066
Abstrakt: Fetoscopy laser photocoagulation is a widely adopted procedure for treating Twin-to-Twin Transfusion Syndrome (TTTS). The procedure involves photocoagulation pathological anastomoses to restore a physiological blood exchange among twins. The procedure is particularly challenging, from the surgeon's side, due to the limited field of view, poor manoeuvrability of the fetoscope, poor visibility due to amniotic fluid turbidity, and variability in illumination. These challenges may lead to increased surgery time and incomplete ablation of pathological anastomoses, resulting in persistent TTTS. Computer-assisted intervention (CAI) can provide TTTS surgeons with decision support and context awareness by identifying key structures in the scene and expanding the fetoscopic field of view through video mosaicking. Research in this domain has been hampered by the lack of high-quality data to design, develop and test CAI algorithms. Through the Fetoscopic Placental Vessel Segmentation and Registration (FetReg2021) challenge, which was organized as part of the MICCAI2021 Endoscopic Vision (EndoVis) challenge, we released the first large-scale multi-center TTTS dataset for the development of generalized and robust semantic segmentation and video mosaicking algorithms with a focus on creating drift-free mosaics from long duration fetoscopy videos. For this challenge, we released a dataset of 2060 images, pixel-annotated for vessels, tool, fetus and background classes, from 18 in-vivo TTTS fetoscopy procedures and 18 short video clips of an average length of 411 frames for developing placental scene segmentation and frame registration for mosaicking techniques. Seven teams participated in this challenge and their model performance was assessed on an unseen test dataset of 658 pixel-annotated images from 6 fetoscopic procedures and 6 short clips. For the segmentation task, overall baseline performed was the top performing (aggregated mIoU of 0.6763) and was the best on the vessel class (mIoU of 0.5817) while team RREB was the best on the tool (mIoU of 0.6335) and fetus (mIoU of 0.5178) classes. For the registration task, overall the baseline performed better than team SANO with an overall mean 5-frame SSIM of 0.9348. Qualitatively, it was observed that team SANO performed better in planar scenarios, while baseline was better in non-planner scenarios. The detailed analysis showed that no single team outperformed on all 6 test fetoscopic videos. The challenge provided an opportunity to create generalized solutions for fetoscopic scene understanding and mosaicking. In this paper, we present the findings of the FetReg2021 challenge, alongside reporting a detailed literature review for CAI in TTTS fetoscopy. Through this challenge, its analysis and the release of multi-center fetoscopic data, we provide a benchmark for future research in this field.
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
(Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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