Medical image segmentation based on deep feature learning and multistage classification - Wang, Xiaohong (Nanyang Technological University, 2020)
Automatic segmentation of target organs in medical image plays a crucial role in the computer-aided diagnosis of human diseases, like retinal vessel from fundus image, and melanoma from dermoscopic image. Manual segmentation of those tissues is a time-consuming and labor-intensive task that is not feasible for clinicians to annotate a large amount of medical images by hand. Thus, a reliable medical image segmentation system with lower cost and less human interaction than the observation-based techniques is attractive in the field of medical image analysis. This thesis investigates medical image segmentation architectures through deep feature learning and multistage classification. Specifically, we proposed two novel multistage classification schemes for retinal vessel segmentation. For more complex and large dermoscopic image data analysis issue, we proposed a novel deep feature learning scheme for skin lesion segmentation. Details of the completed works are summarized as follows: