Automatic Detection and Characterization of Pathological Fluid Regions in Optical Coherence Tomography Images

Abstract

Intraretinal fluid accumulation is both the common symptom and culprit of the main causes of blindness in developed countries: Age-related Macular Degeneration and Diabetic Macular Edema. For its diagnosis, experts of the domain employ Optical Coherence Tomography images (OCT), providing non-invasive cross-sectional representations of the retinal structures. However, like any medical imaging modality, OCT is influenced by multiple factors that impact its quality and subsequent interpretation. Coupled with the subjectiveness of the human experts, these factors can significantly affect the diagnostic process, treatment and quality of life for the affected individuals (particularly in these pathologies where early detection is crucial). To address these challenges, Computer-Aided Diagnosis (CAD) methodologies are developed, offering a layer of abstraction of the information present in the images. Still, in the particular scenario of these pathological fluid accumulations, the development of these methodologies is specially difficult due to their diffuse nature without defined boundaries. In this thesis, we proposed different CAD methodologies with the objective of helping expert clinicians to better detect and understand these pathologies. Furthermore, we expand the developed methodologies to other medical imaging modalities and conditions, such as macular neovascularizations in OCT Angiographies and COVID-19 diagnosis through the analysis of lung chest radiographs.