Deep-Learning-Based Image Registration and Tumor Follow-Up Analysis
- Promotor: B. van Ginneken and H. Hahn
- Copromotor: N. Lessmann and S. Heldmann
- Graduation year: 2022
- Radboud University
This thesis is focused on the development of deep-learning based image registration approaches and on efficient tumor follow-up analysis. Chapter 2 describes a method for a memory-efficient weakly-supervised deep-learning model for multi-modal image registration. The method combines three 2D networks into a 2.5D registration network. Chapter 3 presents a multilevel approach for deep learning-based image registration. Chapter 4 describes a method that incorporates multiple anatomical constraints as anatomical priors into the registration network applied to CT lung registration. Chapter 5 presents the results of the Learn2Reg challenge and compares several conventional and deep-learning-based registration methods. Chapter 6 describes a pipeline that automates the segmentation and measurement of matching lesions, given a point annotation in the baseline lesion. The pipeline is based on a registration approach to locate corresponding image regions and a convolutional neural network to segment the lesion in the follow-up image. Chapter 7 presents the reader study, which investigates whether the assessment time for follow-up lesion segmentations is reduced by AI-assisted workflow while maintaining the same quality of segmentations.