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Deep learning-based fetoscopic mosaicking for field-of-view expansion

Bano, S; Vasconcelos, F; Tella-Amo, M; Dwyer, G; Gruijthuijsen, C; Vander Poorten, E; Vercauteren, T; ... Stoyanov, D; + view all (2020) Deep learning-based fetoscopic mosaicking for field-of-view expansion. International Journal of Computer Assisted Radiology and Surgery 10.1007/s11548-020-02242-8. (In press). Green open access

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Abstract

PURPOSE: Fetoscopic laser photocoagulation is a minimally invasive surgical procedure used to treat twin-to-twin transfusion syndrome (TTTS), which involves localization and ablation of abnormal vascular connections on the placenta to regulate the blood flow in both fetuses. This procedure is particularly challenging due to the limited field of view, poor visibility, occasional bleeding, and poor image quality. Fetoscopic mosaicking can help in creating an image with the expanded field of view which could facilitate the clinicians during the TTTS procedure. METHODS: We propose a deep learning-based mosaicking framework for diverse fetoscopic videos captured from different settings such as simulation, phantoms, ex vivo, and in vivo environments. The proposed mosaicking framework extends an existing deep image homography model to handle video data by introducing the controlled data generation and consistent homography estimation modules. Training is performed on a small subset of fetoscopic images which are independent of the testing videos. RESULTS: We perform both quantitative and qualitative evaluations on 5 diverse fetoscopic videos (2400 frames) that captured different environments. To demonstrate the robustness of the proposed framework, a comparison is performed with the existing feature-based and deep image homography methods. CONCLUSION: The proposed mosaicking framework outperformed existing methods and generated meaningful mosaic, while reducing the accumulated drift, even in the presence of visual challenges such as specular highlights, reflection, texture paucity, and low video resolution.

Type: Article
Title: Deep learning-based fetoscopic mosaicking for field-of-view expansion
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s11548-020-02242-8
Publisher version: https://doi.org/10.1007/s11548-020-02242-8
Language: English
Additional information: © 2020 Springer Nature Switzerland AG. This article is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/.).
Keywords: Deep learning, Surgical vision, Twin-to-twin transfusion syndrome (TTTS), Fetoscopy, Sequential mosaicking
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10108691
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