Efficient Deep Learning for Medical Imaging: Precision Segmentation and Beyond

[eng] This thesis advances automated medical image analysis by introducing four deep learning frameworks that systematically address core technical barriers to clinical deployment, including computational efficiency, variability in image quality, and the robust integration of imaging with clinical data. Through a compendium of research articles, we develop state-of-the-art solutions spanning ultrasound and MRI. First, we present an end-to-end framework for carotid intima-media thickness (CIMT) measurement in ultrasound images, achieving state-of-the-art atherosclerotic plaque characterization while delivering a 20x speed improvement (0.79 to 0.04 seconds per image). The system provides comprehensive outputs, including segmentation masks, automated measurements, and binary plaque detection, eliminating domain-specific post-processing requirements. Secondly, leveraging the features extracted by our end-to-end model, we pioneer their integration into clinical survival models, demonstrating that learned imaging biomarkers significantly enhance cardiovascular risk stratification with a 20% improvement in patient risk reclassification beyond traditional clinical variables. Third, we develop a multilevel EfficientNet-UNet++ architecture for 3D carotid vessel wall segmentation in black-blood MRI that achieves state-of-the-art performance through contextual slice concatenation and resolution optimization. The framework demonstrates optimal performance at 256 x 256 input resolution (6x original size) while maintaining computational efficiency through targeted multilevel processing. Finally, we introduce a quality-aware segmentation framework with custom loss functions for explicit quality modeling during training. When applied to ultrasound colon wall segmentation, this approach achieves a 20% improvement on medium-quality images and a 31% improvement on low-quality images, directly addressing ultrasound's fundamental challenge of variable image quality. Collectively, these contributions establish the technical foundations for robust clinical imaging through efficient segmentation architectures, the integration of imaging with clinical data, explicit quality modeling, and comprehensive clinical validation across two medical imaging modalities.