Deep learning application on head CT images

• Main Author: How, Chun Hung
• Other Authors: Jagath C Rajapakse
• Format: Thesis-Master by Research
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Computer science and engineering
• Online Access: https://hdl.handle.net/10356/172627
• Institution: Nanyang Technological University

I studied on the deep learning frontier application on head Computed Tomography (CT) scans due to the wide adoption and cost efficiency of CT scans. The works were formulated in 4 chapters; In Chapter 1, I introduced the technical knowledge of CT images and the medical problems being solved using deep learning. In Chapter 2, I dived into the problem of image segmentation for brain intracranial hemorrhage (ICH) with small annotated mask dataset. My proposed training framework Meta Pseudo Segmentation (MPS) trained segmentation model with consistency training and student-teacher learning, outperforming supervised learning and EM algorithm. In Chapter 3, I tackled the problem of pseudo-healthy generation using VQGAN. My method outperformed the previous work substantially in synthesis quality. In Chapter 4, I proposed a unified multi-task segmentation model to perform ICH segmentation and brain tissue segmentation on CT. My model performed best in segmenting complex and granular region.