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Real-time mosaicing of fetoscopic videos using SIFT

Daga, P; Chadebecq, F; Shakir, DCS; Garcia Peraza Herrera, L; Tella Amo, M; Dwyer, G; David, AL; ... Ourselin, S; + view all (2016) Real-time mosaicing of fetoscopic videos using SIFT. Proceedings of SPIE , 9786 , Article 97861R. 10.1117/12.2217172. Green open access

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Abstract

Fetoscopic laser photo-coagulation of the placental vascular anastomoses remains the most effective therapy for twin-to-twin transfusion syndrome (TTTS) in monochorionic twin pregnancies. However, to ensure the success of the intervention, complete photo-coagulation of all anastomoses is needed. This is made difficult by the limited field of view of the fetoscopic video guidance, which hinders the surgeon’s ability to locate all the anastomoses. A potential solution to this problem is to expand the field of view of the placental surface by creating a mosaic from overlapping fetoscopic images. This mosaic can then be used for anastomoses localization and spatial orientation during surgery. However, this requires accurate and fast algorithms that can operate within the real-time constraints of fetal surgery. In this work, we present an image mosaicing framework that leverages the parallelism of modern GPUs and can process clinical fetoscopic images in real-time. Initial qualitative results on ex-vivo placental images indicate that the proposed framework can generate clinically useful mosaics from fetoscopic videos in real-time.

Type: Article
Title: Real-time mosaicing of fetoscopic videos using SIFT
Location: San Diego, CA
Open access status: An open access version is available from UCL Discovery
DOI: 10.1117/12.2217172
Publisher version: http://dx.doi.org/10.1117/12.2217172
Language: English
Additional information: Copyright © 2016 Society of Photo Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.
Keywords: Image stitching, Mosaicing, SIFT, image-guided surgery, GPU, CUDA, feature extraction
UCL classification: UCL > School of Life and Medical Sciences
UCL > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute for Women's Health > Maternal and Fetal Medicine
UCL > School of BEAMS
UCL > School of BEAMS > Faculty of Engineering Science
URI: http://discovery.ucl.ac.uk/id/eprint/1476244
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