  In Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in
the monochorionic placenta can produce uneven blood flow between the two
fetuses. In the current practice, TTTS is treated surgically by closing
abnormal anastomoses using laser ablation. This surgery is minimally invasive
and relies on fetoscopy. Limited field of view makes anastomosis identification
a challenging task for the surgeon. To tackle this challenge, we propose a
learning-based framework for in-vivo fetoscopy frame registration for
field-of-view expansion. The novelties of this framework relies on a
learning-based keypoint proposal network and an encoding strategy to filter (i)
irrelevant keypoints based on fetoscopic image segmentation and (ii)
inconsistent homographies. We validate of our framework on a dataset of 6
intraoperative sequences from 6 TTTS surgeries from 6 different women against
the most recent state of the art algorithm, which relies on the segmentation of
placenta vessels. The proposed framework achieves higher performance compared
to the state of the art, paving the way for robust mosaicking to provide
surgeons with context awareness during TTTS surgery.
