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Toward a navigation framework for fetoscopy
PURPOSE: Fetoscopic laser photocoagulation of placental anastomoses is the most effective treatment for twin-to-twin transfusion syndrome (TTTS). A robust mosaic of placenta and its vascular network could support surgeons’ exploration of the placenta by enlarging the fetoscope field-of-view. In this...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632301/ https://www.ncbi.nlm.nih.gov/pubmed/37587389 http://dx.doi.org/10.1007/s11548-023-02974-3 |
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author | Casella, Alessandro Lena, Chiara Moccia, Sara Paladini, Dario De Momi, Elena Mattos, Leonardo S. |
author_facet | Casella, Alessandro Lena, Chiara Moccia, Sara Paladini, Dario De Momi, Elena Mattos, Leonardo S. |
author_sort | Casella, Alessandro |
collection | PubMed |
description | PURPOSE: Fetoscopic laser photocoagulation of placental anastomoses is the most effective treatment for twin-to-twin transfusion syndrome (TTTS). A robust mosaic of placenta and its vascular network could support surgeons’ exploration of the placenta by enlarging the fetoscope field-of-view. In this work, we propose a learning-based framework for field-of-view expansion from intra-operative video frames. METHODS: While current state of the art for fetoscopic mosaicking builds upon the registration of anatomical landmarks which may not always be visible, our framework relies on learning-based features and keypoints, as well as robust transformer-based image-feature matching, without requiring any anatomical priors. We further address the problem of occlusion recovery and frame relocalization, relying on the computed features and their descriptors. RESULTS: Experiments were conducted on 10 in-vivo TTTS videos from two different fetal surgery centers. The proposed framework was compared with several state-of-the-art approaches, achieving higher [Formula: see text] on 7 out of 10 videos and a success rate of [Formula: see text] in occlusion recovery. CONCLUSION: This work introduces a learning-based framework for placental mosaicking with occlusion recovery from intra-operative videos using a keypoint-based strategy and features. The proposed framework can compute the placental panorama and recover even in case of camera tracking loss where other methods fail. The results suggest that the proposed framework has large potential to pave the way to creating a surgical navigation system for TTTS by providing robust field-of-view expansion. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-023-02974-3. |
format | Online Article Text |
id | pubmed-10632301 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Springer International Publishing |
record_format | MEDLINE/PubMed |
spelling | pubmed-106323012023-11-14 Toward a navigation framework for fetoscopy Casella, Alessandro Lena, Chiara Moccia, Sara Paladini, Dario De Momi, Elena Mattos, Leonardo S. Int J Comput Assist Radiol Surg Original Article PURPOSE: Fetoscopic laser photocoagulation of placental anastomoses is the most effective treatment for twin-to-twin transfusion syndrome (TTTS). A robust mosaic of placenta and its vascular network could support surgeons’ exploration of the placenta by enlarging the fetoscope field-of-view. In this work, we propose a learning-based framework for field-of-view expansion from intra-operative video frames. METHODS: While current state of the art for fetoscopic mosaicking builds upon the registration of anatomical landmarks which may not always be visible, our framework relies on learning-based features and keypoints, as well as robust transformer-based image-feature matching, without requiring any anatomical priors. We further address the problem of occlusion recovery and frame relocalization, relying on the computed features and their descriptors. RESULTS: Experiments were conducted on 10 in-vivo TTTS videos from two different fetal surgery centers. The proposed framework was compared with several state-of-the-art approaches, achieving higher [Formula: see text] on 7 out of 10 videos and a success rate of [Formula: see text] in occlusion recovery. CONCLUSION: This work introduces a learning-based framework for placental mosaicking with occlusion recovery from intra-operative videos using a keypoint-based strategy and features. The proposed framework can compute the placental panorama and recover even in case of camera tracking loss where other methods fail. The results suggest that the proposed framework has large potential to pave the way to creating a surgical navigation system for TTTS by providing robust field-of-view expansion. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11548-023-02974-3. Springer International Publishing 2023-08-16 2023 /pmc/articles/PMC10632301/ /pubmed/37587389 http://dx.doi.org/10.1007/s11548-023-02974-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Original Article Casella, Alessandro Lena, Chiara Moccia, Sara Paladini, Dario De Momi, Elena Mattos, Leonardo S. Toward a navigation framework for fetoscopy |
title | Toward a navigation framework for fetoscopy |
title_full | Toward a navigation framework for fetoscopy |
title_fullStr | Toward a navigation framework for fetoscopy |
title_full_unstemmed | Toward a navigation framework for fetoscopy |
title_short | Toward a navigation framework for fetoscopy |
title_sort | toward a navigation framework for fetoscopy |
topic | Original Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10632301/ https://www.ncbi.nlm.nih.gov/pubmed/37587389 http://dx.doi.org/10.1007/s11548-023-02974-3 |
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