Automatic Initialization for 3D Ultrasound CT Registration During Liver Tumor Ablations

Master thesis (2018)

Authors

D.E. Schut Electrical Engineering, Mathematics and Computer Science

Contributors

Theo Van Walsum (mentor)
R.F. Remis (mentor)
Marius Staring (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2018 Dirk Schut
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Published Date

29-08-2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Ablation is a medical procedure to treat liver cancer where a needle-like catheter has to be inserted into a tumor, which will then be heated or frozen to destroy the tumor tissue. To guide the catheter, Ultrasound(US) imaging is used which shows the catheter position in real time. However, some tumors are not visible on US images. To make these tumors visible, image fusion can be used between the inter-operative US image and a pre-operative contrast enhanced CT(CECT) scan, on which the tumors are visible. Several methods exist for tracking the motions of the US transducer relative to the CECT scan, but they all require a manual initialization or external tracking hardware to align the coordinate systems of both scans. In this thesis we present a technique for finding an initialization using only the image data. To achieve this, deep learning is used to segment liver vessels and the boundary of the liver in 3D US images. To find the rigid transformation parameters, the SaDE evolutionary algorithm was used to optimize the alignment between the blood vessels and the liver boundary between both scans.