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Deep learning-based monocular placental pose estimation: towards collaborative robotics in fetoscopy.

Mirza Awais Ahmad¹, Mouloud Ourak², Caspar Gruijthuijsen²

¹Department of Mechanical Engineering, KU Leuven, Leuven, Belgium. mirzaawais.ahmad@kuleuven.be.

International Journal of Computer Assisted Radiology and Surgery

|May 1, 2020

Summary

This study introduces a minimal robotic system for twin-to-twin transfusion syndrome (TTTS) treatment. A convolutional neural network (CNN) enables semi-autonomous fetoscopy, improving procedural alignment and safety for complex cases.

Keywords:

Convolutional neural networks Deep learning Fetoscopy Orientation estimation Robot assisted surgery Shared control

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Area of Science:

Medical Robotics
Surgical Technology
Fetal Surgery

Background:

Twin-to-twin transfusion syndrome (TTTS) is a serious placental defect in monochorionic twin pregnancies.
Current fetoscopic laser ablation (ELA) techniques for TTTS have limitations in complex cases, impacting procedural success.
Robot-assisted technology offers potential to enhance dexterity and improve safety in fetoscopic procedures.

Purpose of the Study:

To develop a 'minimal' robotic system for TTTS treatment, enhancing existing fetoscopic ELA.
To create an automatic placental pose estimation system for semi-autonomous fetoscopy.
To improve visualization, instrument angulation, and safety during TTTS interventions.

Main Methods:

A compact, 'robotised' instrument was developed for efficient use in TTTS procedures.
A convolutional neural network (CNN) was trained for automatic placental pose estimation from fetoscopic images.
The CNN was trained using simulated data and in-silico models, augmented with real intervention data for domain adaptation.

Main Results:

The trained CNN demonstrated promising results on synthetic, phantom, and in vivo patient data.
A virtual reality simulator evaluated the network's performance in autonomously controlling a flexible distal tip.
Improved alignment was achieved compared to manual operations in the simulator.

Conclusions:

The CNN-based pose estimator is feasible for real-time shared control in fetoscopic applications.
This approach can enhance the safety and effectiveness of therapeutic procedures for TTTS.
Minimal robotic systems offer a practical solution for complex fetoscopic interventions.