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bioRxiv · 10.1101/2025.10.09.681509

Kidney Tissue Characterization using Normalized Raman Imaging and Segment-Anything

Karmakar, R.·Trim, W.·Kirschner, M.·Noerrelykke, S.
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Abstract

Normalized Raman Imaging (NoRI) enables high-resolution, label-free quantification of protein and lipid concentrations in biological tissues. Because NoRI provides rich molecular information, the analysis of its large, multi-channel datasets turns into a significant computational bottleneck. In this work, we introduce a novel, modular computational pipeline for automated segmentation and quantification of kidney tissue structures imaged with NoRI. The pipeline integrates classical image processing with state-of-the-art machine learning tools, including the Segment Anything Model (SAM) and ilastik, to segment key anatomical and biochemical features--such as tubules, nuclei, brush borders, and lumens. A custom contrast-enhancement strategy was developed to create a third SAM input channel from NoRI data, leading to a substantial improvement in segmentation performance (F1 score: 0.9226). Our framework enables accurate cytoplasm, resolved quantification of protein and lipid concentrations and reveals distinct biochemical signatures across renal tubule subtypes and experimental conditions. This method offers a robust, scalable foundation for quantitative tissue analysis and enhances the utility of NoRI imaging for biomedical research.

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Karmakar, R., Trim, W., Kirschner, M., Noerrelykke, S.. 2025-10-11. Kidney Tissue Characterization using Normalized Raman Imaging and Segment-Anything. https://doi.org/10.1101/2025.10.09.681509

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