Deep learning-based fetoscopic mosaicking for field-of-view expansion
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ORIGINAL ARTICLE
Deep learning-based fetoscopic mosaicking for field-of-view expansion Sophia Bano1 · Francisco Vasconcelos1 · Marcel Tella-Amo1 · George Dwyer1 · Caspar Gruijthuijsen2 · Emmanuel Vander Poorten2 · Tom Vercauteren3 · Sebastien Ourselin3 · Jan Deprest4 · Danail Stoyanov1 Received: 13 April 2020 / Accepted: 30 July 2020 © The Author(s) 2020
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. Keywords Deep learning · Surgical vision · Twin-to-twin transfusion syndrome (TTTS) · Fetoscopy · Sequential mosaicking
Introduction This paper is based on the work: “Bano, S., Vasconcelos, F., Amo, M.T., Dwyer, G., Gruijthuijsen, C., Deprest, J., Ourselin, S., Vander Poorten, E., Vercauteren, T. and Stoyanov, D., 2019, October. Deep sequential mosaicking of fetoscopic videos. In: Shen D. et al. (eds) Medical Image Computing and Computer Assisted Intervention-MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science, vol 11764. Springer, Cham.”.
Twin-to-twin transfusion syndrome (TTTS) is a rare condition during pregnancy that affects 10–15% of genetically identical twins sharing a monochorionic placenta [5]. It is caused by abnormal placental vascular anastomoses on the chorionic plate of the placenta resulting in uneven flow of
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s11548-020-02242-8) contains supplementary material, which is available to authorized users.
1
Wellcome/EPSRC Centre for Interventional
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