  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.
