MPhil Thesis
Background
This is my thesis which was written during my time at UCL studying toward my MPhil in Machine Learning: “Unsupervised Learning for Generalised Super-Resolution of 3D Anisotropic Medical Images via Domain Transfer”.
Abstract
Three-dimensional (3D) imaging is popular in medical applications, however, anisotropic 3D volumes with thick, low-spatial-resolution slices are often acquired to reduce scan times. Deep learning (DL) offers a solution to recover high- resolution features through super-resolution reconstruction (SRR). Unfortunately, paired training data is unavailable in many 3D medical applications and therefore a novel unpaired approach is proposed; CLADE (Cycle Loss Augmented Degrada- tion Enhancement). CLADE uses a modified CycleGAN-based architecture with a cycle-consistent gradient mapping loss and weight demodulation process. This approach is trained in an unsupervised fashion to learn SRR of the low-resolution dimension, from disjoint patches of the high-resolution plane within the anisotropic 3D volume data itself via domain transfer. The feasibility of CLADE in abdom- inal Magnetic Resonance Imaging (MRI) and abdominal Computed Tomography (CT) imaging is demonstrated, with significant improvements in CLADE image quality over low-resolution volumes, conventional Cycle-GAN and state-of-the-art self-supervised SRR; SMORE (Synthetic Multi-Orientation Resolution Enhance- ment).
Full Text
I will amend full-text links here, once the thesis has been published internally by UCL.
Michele Pascale
PhD Student in Mathematics @ Queen Mary University of London