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Related Experiment Video

Updated: Jan 2, 2026

Model Surgical Training: Skills Acquisition in Fetoscopic Laser Photocoagulation of Monochorionic Diamniotic Twin Placenta Using Realistic Simulators

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Inter-foetus Membrane Segmentation for TTTS Using Adversarial Networks.

Alessandro Casella^1,2, Sara Moccia^3,4, Emanuele Frontoni⁴

¹Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy. alessandro.casella@polimi.it.

Annals of Biomedical Engineering

|December 7, 2019

Summary

This summary is machine-generated.

This study introduces an AI-driven method for automatically segmenting the inter-foetal membrane during fetoscopic surgery for Twin-to-Twin Transfusion Syndrome. The novel adversarial network significantly improves membrane identification accuracy, aiding surgeons in complex procedures.

Keywords:

Adversarial networks Deep learning Fetoscopy Intraoperative-image segmentation

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Area of Science:

Medical Imaging
Surgical Technology
Artificial Intelligence

Background:

Twin-to-Twin Transfusion Syndrome (TTTS) necessitates minimally invasive fetoscopic laser surgery.
Accurate identification of the inter-foetal membrane is crucial for locating abnormal vascular anastomoses during TTTS surgery.
Current membrane identification methods face challenges due to the fetoscopic environment's limitations.

Purpose of the Study:

To develop an automatic and rapid membrane segmentation technique for fetoscopic images.
To enhance surgical precision and safety in TTTS treatment.
To provide surgeons with a reliable tool for intraoperative membrane identification.

Main Methods:

Implementation of an adversarial network comprising two Fully-Convolutional Neural Networks (FCNNs).
A segmentation network inspired by U-Net, enhanced with residual blocks.
A critic network utilizing the encoding path of the segmentor.
Validation using a dataset of 900 labeled images from 6 surgical cases.

Main Results:

The proposed adversarial network achieved a median Dice similarity coefficient of 91.91% (IQR: 4.63%).
This performance significantly outperformed traditional U-Net (82.98%-IQR: 14.41%) and U-Net with residual blocks (86.13%-IQR: 13.63%) approaches.
The system demonstrated robustness and high accuracy in segmenting the inter-foetal membrane.

Conclusions:

The developed adversarial network offers a valuable and robust solution for automatic membrane segmentation in fetoscopic surgery.
This technology can assist surgeons in precise membrane identification, improving outcomes for Twin-to-Twin Transfusion Syndrome.
The AI-driven approach holds promise for advancing minimally invasive fetal surgery techniques.