eprintid: 10107230 rev_number: 23 eprint_status: archive userid: 608 dir: disk0/10/10/72/30 datestamp: 2020-10-27 10:08:11 lastmod: 2021-12-06 23:43:14 status_changed: 2020-10-27 10:08:11 type: proceedings_section metadata_visibility: show creators_name: Bano, S creators_name: Vasconcelos, F creators_name: Shepherd, LM creators_name: Vander Poorten, E creators_name: Vercauteren, T creators_name: Ourselin, S creators_name: David, AL creators_name: Deprest, J creators_name: Stoyanov, D title: Deep Placental Vessel Segmentation for Fetoscopic Mosaicking ispublished: pub divisions: UCL divisions: B02 divisions: D11 divisions: G12 divisions: B04 divisions: C05 divisions: F48 divisions: F42 divisions: B03 divisions: C03 keywords: Fetoscopy · Deep learning · Vessel segmentation · Vessel registration · Mosaicking · Twin-to-twin transfusion syndrome note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. 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. date: 2020-09-29 publisher: Springer official_url: https://doi.org/10.1007/978-3-030-59716-0_73 oa_status: green full_text_type: other language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 1796629 doi: 10.1007/978-3-030-59716-0_73 isbn_13: 9783030597153 lyricists_name: Bano, Sophia lyricists_name: David, Anna lyricists_name: Porto Guerra E Vasconcelos, Francisco lyricists_name: Shepherd, Luke lyricists_name: Stoyanov, Danail lyricists_name: Vercauteren, Tom lyricists_id: SBANO36 lyricists_id: ADAVI52 lyricists_id: FVASC02 lyricists_id: LMSHE34 lyricists_id: DSTOY26 lyricists_id: TVERC65 actors_name: Bano, Sophia actors_id: SBANO36 actors_role: owner full_text_status: public series: Lecture Notes in Computer Science publication: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) volume: 12263 place_of_pub: Lima, Peru pagerange: 763-773 event_title: International Conference on Medical Image Computing and Computer-Assisted Intervention issn: 1611-3349 book_title: Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 editors_name: Martel, A editors_name: Abolmaesumi, P editors_name: Stoyanov, D editors_name: Mateus, D editors_name: Zuluaga, M editors_name: Zhou, SK editors_name: Racoceanu, D editors_name: Joskowicz, L citation: Bano, S; Vasconcelos, F; Shepherd, LM; Vander Poorten, E; Vercauteren, T; Ourselin, S; David, AL; ... Stoyanov, D; + view all <#> Bano, S; Vasconcelos, F; Shepherd, LM; Vander Poorten, E; Vercauteren, T; Ourselin, S; David, AL; Deprest, J; Stoyanov, D; - view fewer <#> (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 document_url: https://discovery.ucl.ac.uk/id/eprint/10107230/1/2007.04349v1.pdf