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

Bano, Sophia; Vasconcelos, Francisco; Tella-Amo, Marcel; Dwyer, George; Gruijthuijsen, Caspar; Vander Poorten, Emmanuel; Vercauteren, Tom; Ourselin, Sebastien; Deprest, Jan; Stoyanov, Danail

doi:10.1007/s11548-020-02242-8

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

Author:

Bano, Sophia

Vasconcelos, Francisco ; Tella-Amo, Marcel ; Dwyer, George ; Gruijthuijsen, Caspar ; Vander Poorten, Emmanuel ; Vercauteren, Tom ; Ourselin, Sebastien ; Deprest, Jan ; Stoyanov, Danail

Keywords:

Science & Technology, Technology, Life Sciences & Biomedicine, Engineering, Biomedical, Radiology, Nuclear Medicine & Medical Imaging, Surgery, Engineering, Deep learning, Surgical vision, Twin-to-twin transfusion syndrome (TTTS), Fetoscopy, Sequential mosaicking, Computer Simulation, Deep Learning, Female, Fetofetal Transfusion, Humans, Laser Coagulation, Phantoms, Imaging, Placenta, Pregnancy, 1103 Clinical Sciences, Nuclear Medicine & Medical Imaging, 3202 Clinical sciences, 4603 Computer vision and multimedia computation

Abstract:

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.