This thesis presents my original research on assessing human soft tissue pathologies through the application of artificial intelligence (AI) to medical imaging. The significance of this work lies in the development of automated systems that enhance the understanding of human soft tissue-related pathologies. These systems aim to support more reliable clinical decision-making and enable earlier interventions by minimizing human error and variability, ultimately improving patient outcomes.
This study presents an end-to-end tendon pathology detection (classification) module utilizing a custom ankle MRI dataset comprising 76 subjects (45 healthy, 31 pathological). We propose a graph-based module that converts images into graph representations for classification.
This study proposes a comprehensive end-to-end tendon segmentation module composed of a preliminary superpixel-based coarse segmentation preceding the final segmentation task.