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Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

Human Fetal Blood Flow Quantification with Magnetic Resonance Imaging and Motion Compensation

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

Alessandro Casella¹, Sara Moccia², Dario Paladini³

¹Department of Advanced Robotics, Istituto Italiano di Tecnologia, Genoa, Italy; Department of Electronics, Information and Bioengineering, Politecnico di Milano, Milan, Italy.

Medical Image Analysis

|March 1, 2021

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This summary is machine-generated.

This study introduces a deep learning framework to automatically segment the inter-fetal membrane in videos during Twin-to-Twin Transfusion Syndrome (TTTS) surgery. The new method improves surgical guidance and accuracy for this complex procedure.

Area of Science:

Medical Imaging
Computer Vision
Surgical Technology

Background:

Twin-to-Twin Transfusion Syndrome (TTTS) involves abnormal placental vascular anastomoses causing uneven fetal blood flow.
Current surgical treatment uses laser ablation, with surgeons relying on the inter-fetal membrane for reference.
Identifying the membrane is challenging due to limited fetoscopic view, low image quality, and variable illumination.

Purpose of the Study:

To develop a deep learning framework for automatic inter-fetal membrane segmentation in fetoscopic videos.
To overcome limitations of current tools in segmenting membranes with homogeneous texture and variable illumination.

Main Methods:

A novel deep learning framework incorporating an instance-normalized dense block for illumination invariance and spatio-temporal feature extraction.

Keywords:

Deep learning Fetoscopy Inter-fetal membrane Twin-to-Twin transfusion syndrome (TTTS)

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Main Results:

The framework achieved a mean Dice Similarity Coefficient of 0.8780±0.1383 in validation.
Comprehensive validation was performed on 20 videos (2000 frames) from 20 surgeries.

Conclusions:

The proposed framework shows significant potential for improving Twin-to-Twin Transfusion Syndrome surgical practice.
It can enable surgical guidance systems, enhance context awareness, and potentially reduce surgery duration.