A shape-constraint adversarial framework with instance-normalized spatio-temporal features for inter-fetal membrane segmentation

Abstract

Background and objectives: During Twin-to-Twin Transfusion Syndrome (TTTS), abnormal vascular anastomoses in the monochorionic placenta can produce uneven blood flow between the fetuses. In the current practice, this syndrome is surgically treated by closing the abnormal connections using laser ablation. Surgeons commonly use the inter-fetal membrane as a reference. Limited field of view, low fetoscopic image quality and high inter-subject variability make the membrane identification a challenging task. However, currently available tools are not optimal for automatic membrane segmentation in fetoscopic videos, due to membrane texture homogeneity and high illumination variability.

Methods: To tackle these challenges, we present a new deep-learning framework for inter-fetal membrane segmentation on in-vivo fetoscopic videos. The framework enhances existing architectures by (i) encoding a novel (instance-normalized) dense block, invariant to illumination changes, that extracts spatio-temporal features to enforce pixel connectivity in time, and (ii) relying on an adversarial training, which constrains macro appearance.

Results: We performed a comprehensive validation using 20 different videos (2000 frames) from 20 different surgeries, achieving a mean Dice Similarity Coefficient of 0.8780±0.1383.

Conclusions: The proposed framework has great potential to positively impact the actual surgical practice for TTTS treatment, allowing the implementation of surgical guidance systems that can enhance context awareness and potentially lower the duration of the surgeries.

Keywords: Deep learning; Fetoscopy; Inter-fetal membrane; Twin-to-Twin transfusion syndrome (TTTS).