Semi-supervised Learning for Medical Image Segmentation - Li, Ruizhe (2023)
Medical image segmentation is a fundamental step in many computer aided clinical applications, such as tumour detection and quantification, organ measurement and feature learning, etc. However, manually delineating the target of interest on medical images (2D and 3D) is highly labour intensive and time-consuming, even for clinical experts. To address this problem, this thesis focuses on exploring and developing solutions of interactive and fully automated methods to achieve efficient and accurate medical image segmentation.