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Deep Placental Vessel Segmentation for Fetoscopic Mosaicking

Bano, S; Vasconcelos, F; Shepherd, LM; Vander Poorten, E; Vercauteren, T; Ourselin, S; David, AL; ... Stoyanov, D; + view all (2020) Deep Placental Vessel Segmentation for Fetoscopic Mosaicking. In: Martel, A and Abolmaesumi, P and Stoyanov, D and Mateus, D and Zuluaga, M and Zhou, SK and Racoceanu, D and Joskowicz, L, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. (pp. pp. 763-773). Springer: Lima, Peru. Green open access

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Abstract

During fetoscopic laser photocoagulation, a treatment for twin-to-twin transfusion syndrome (TTTS), the clinician first identifies abnormal placental vascular connections and laser ablates them to regulate blood flow in both fetuses. The procedure is challenging due to the mobility of the environment, poor visibility in amniotic fluid, occasional bleeding, and limitations in the fetoscopic field-of-view and image quality. Ideally, anastomotic placental vessels would be automatically identified, segmented and registered to create expanded vessel maps to guide laser ablation, however, such methods have yet to be clinically adopted. We propose a solution utilising the U-Net architecture for performing placental vessel segmentation in fetoscopic videos. The obtained vessel probability maps provide sufficient cues for mosaicking alignment by registering consecutive vessel maps using the direct intensity-based technique. Experiments on 6 different in vivo fetoscopic videos demonstrate that the vessel intensity-based registration outperformed image intensity-based registration approaches showing better robustness in qualitative and quantitative comparison. We additionally reduce drift accumulation to negligible even for sequences with up to 400 frames and we incorporate a scheme for quantifying drift error in the absence of the ground-truth. Our paper provides a benchmark for fetoscopy placental vessel segmentation and registration by contributing the first in vivo vessel segmentation and fetoscopic videos dataset.

Type: Proceedings paper
Title: Deep Placental Vessel Segmentation for Fetoscopic Mosaicking
Event: International Conference on Medical Image Computing and Computer-Assisted Intervention
ISBN-13: 9783030597153
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-59716-0_73
Publisher version: https://doi.org/10.1007/978-3-030-59716-0_73
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Fetoscopy · Deep learning · Vessel segmentation · Vessel registration · Mosaicking · Twin-to-twin transfusion syndrome
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > UCL EGA Institute for Womens Health > Maternal and Fetal Medicine
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
UCL > Provost and Vice Provost Offices > UCL SLASH
UCL > Provost and Vice Provost Offices > UCL SLASH > Faculty of S&HS
URI: https://discovery.ucl.ac.uk/id/eprint/10107230
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