Deep-Learning Architectures for Placenta Vessel Segmentation in TTTS Fetoscopic Images

Twin-to-Twin Transfusion Syndrome (TTTS) is a rare pregnancy pathology affecting identical twins, which share both the placenta and a network of blood vessels. Sharing blood vessels implies an unbalanced oxygen and nutrients supply between one twin (the donor) and the other (the recipient). Endoscopic laser ablation, a fetoscopic minimally invasive procedure, is performed to treat TTTS by restoring a physiological blood supply to both twins lowering mortality and morbidity rates. TTTS is a challenging procedure, where the surgeons have to recognize and ablate pathological vessels having a very limited view of the surgical size. To provide TTTS surgeons with context awareness, in this work, we investigate the problem of automatic vessel segmentation in fetoscopic images. We evaluated different deep-learning models currently available in the literature, including U-Net, U-Net++ and Feature Pyramid Networks (FPN). We tested several backbones (i.e. ResNet, DenseNet and DPN), for a total of 9 experiments. With a comprehensive evaluation on a novel dataset of 18 videos (1800 frames) from 18 different TTTS surgeries, we obtained a mean intersection-over-union of 0.63 +/- 0.19 using U-Net++ model with DPN backbone. Such results suggest that deep-learning may be a valuable tool for supporting surgeons in vessel identification during TTTS.