Image registration for placenta reconstruction

Conference paper (2016)

Authors

J.C. Gaiser Biomechatronics & Human-Machine Control - Mechanical, Maritime and Materials Engineering
P.P. Jonker OLD Intelligent Vehicles & Cognitive Robotics
T Chiba Nihon University
Research Group
Biomechatronics & Human-Machine Control (Mechanical, Maritime and Materials Engineering) (TU Delft)
Image registration Robustness Feature extraction Image reconstruction Training Surgery Transforms
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Published Date

2016

Language

English

Reuse Rights

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Faculty

Mechanical, Maritime and Materials Engineering

Department

Biomechanical Engineering

Research Group

Biomechatronics & Human-Machine Control

Abstract

In this paper we introduce a method to handle the challenges posed by image registration for placenta reconstruction from fetoscopic video as used in the treatment of Twinto-Twin Transfusion Syndrome (TTTS). Panorama reconstruction of the placenta greatly supports the surgeon in obtaining a complete view of the placenta to localize vascular anastomoses. The found shunts can subsequently be blocked by coagulation in the correct order. By using similarity learning in training a Convolutional Neural Network we created a novel feature extraction method, allowing robust matching of keypoints for image registration and therefore taking the most critical step in placenta reconstruction from fetoscopic video. The fetoscopic video we used for our experiments was acquired from a training simulator for TTTS surgery. We compared our method with state-of-the-art methods. The matching performance of our method is up to three times better while the mean projection error is reduced with 64% for the registered images. Our image registration method provides the ground work for a complete panorama reconstruction of the placenta.