International Journal of Science, Engineering and TechnologyAuthors: Jemimah, Dr. M. Jeyasutha, Associate Professor, Dr. F. Ramesh Dhanaseelan, Professor
Abstract: Ultra-wideband raster-scan optoacoustic mesoscopy (RSOM) is an advanced imaging modality that has shown exceptional capability in visualizing in-vivo epidermal and dermal structures with high resolution. Despite its promise, the automatic and quantitative analysis of three-dimensional RSOM datasets remains largely unaddressed. In this study, we introduce DeepRAP (Deep Learning RSOM Analysis Pipeline), a novel framework designed to analyze and quantify morphological skin features from RSOM images and extract clinically relevant imaging biomarkers for disease characterization. DeepRAP employs a multi-network segmentation strategy based on convolutional neural networks (CNNs) enhanced through transfer learning. This architecture facilitates the automatic identification of skin layers and precise segmentation of the dermal microvasculature, achieving performance on par with expert human annotation. The framework was validated against manual segmentation using RSOM data from 25 psoriasis patients undergoing treatment. The extracted biomarkers successfully characterized disease severity and progression, showing a strong correlation with physician assessments and histological data. In a distinct validation experiment, DeepRAP was applied to a timeseries dataset capturing occlusion-induced hyperemia in 10 healthy volunteers. The framework effectively tracked changes in microvascular biomarkers during occlusion and subsequent reperfusion, demonstrating both high accuracy and reproducibility. Additionally, analysis of a cohort of 75 individuals revealed a significant association between microvascular features in the dermal layer and age, with fine vascular patterns showing the strongest age-related correlation. These findings highlight DeepRAP’s potential to automate and accelerate in-vivo skin analysis, offering a non-invasive alternative to traditional biopsybased methods. The framework enhances the clinical and translational relevance of RSOM by enabling high-throughput, quantitative assessment of skin morphology and vascular health.
DOI: http://doi.org/10.5281/zenodo.15806478
