Ophthalmic clinical trials that study treatment efficacy of eye diseases are performed with a specific purpose and a set of procedures that are predetermined before trial initiation. Hence, they result in a controlled data collection process with gradual changes in the state of a diseased eye. In general, these data include 1D clinical measurements and 3D optical coherence tomography (OCT) imagery. Physicians interpret structural biomarkers for every patient using the 3D OCT images and clinical measurements to make personalized decisions for every patient. Two main challenges in medical image processing has been generalization and personalization. Generalization aims to develop algorithms that work well across diverse patients and scenarios, providing standardized and widely applicable solutions. Personalization, in contrast, tailors algorithms to individual patients based on their unique characteristics, optimizing diagnosis and treatment planning. Generalization offers broad applicability but may overlook individual variations. Personalization provides tailored solutions but requires patient-specific data. While deep learning has shown an affinity towards generalization, it is lacking in personalization. The presence and absence of biomarkers is a personalization challenge rather than a generalization challenge. The variation within OCT scans of patients between visits can be minimal while the difference in manifestation of the same disease across patients may be substantial. The domain difference between OCT scans can arise due to pathology manifestation across patients, clinical labels, and the visit along the treatment process when the scan is taken. Morphological, texture, statistical and fuzzy image processing techniques through adaptive thresholds and preprocessing may prove substantial to overcome these fine-grained challenges. This challenge provides the data and application to address personalization.

{"references": ["M. Prabhushankar, K. Kokilepersaud*, Y. Logan*, S. Trejo Corona*, G. AlRegib, C. Wykoff, \"OLIVES Dataset: Ophthalmic Labels for Investigating Visual Eye Semantics,\" in Advances in Neural Information Processing Systems (NeurIPS 2022) Track on Datasets and Benchmarks, New Orleans, LA,, Nov. 29 \u2013 Dec. 1 2022"]}