"Supervised Learning in Medical Image Registration"

Hessam Sokooti

Abstract

The aim of this thesis is to develop a learning-based image registration method as a much faster alternative to conventional methods without requiring hyper-parameter tuning. We also aimed to improve accuracy as well as inference time of registration misalignment detection methods, via a fully automatic solution. Although all the proposed methods in this thesis are generic, all the experiments are performed on chest CT scans.

Chapter 2 presents a novel method to solve nonrigid image registration through a learning approach, instead of via iterative optimization of a predefined dissimilarity metric. Chapter 3 extends chapter 2 into a practical pipeline based on efficient supervised learning from artificial deformations. Chapter 4 proposes a new automatic method to predict the registration error in a quantitative manner and is applied to chest CT scans. Chapter 5 presents a supervised method to predict registration misalignment using convolutional neural networks (CNNs).

BibTeX entry