SEGMENTATION OF SCEROTIC AND NON-SCEROTIC RENAL BIOPSIES USING IMAGE J - Rani U, Sudha; Subhas C, (ShodhKosh: Journal of Visual and Performing Arts, 2023)
Accurate identification and classifica- tion of glomeruli in renal biopsy specimens are fundamental for histopathological diagnosis and chronic kidney disease staging. While deep learning (DL) methodologies have advanced au- tomated segmentation, their reliance on exten- sive computational resources, large annotated datasets, and specialized expertise limits acces- sibility. This study introduces a standardized, open-source framework for glomerular segmenta- tion and sclerosis classification using ImageJ, cir- cumventing these barriers. Our pipeline inte- grates preprocessing, segmentation, and quantita- tive morphometric analysis to discriminate scle- rotic from non-sclerotic glomeruli based on struc- tural and textural biomarkers. The proposed method was validated using established perfor- mance metrics—Accuracy, Precision, Recall, and Intersection-over-Union (IoU)—on renal biopsy images. When benchmarked against contempo- rary DL-based segmentation techniques, our Im- ageJ workflow achieved comparable efficacy, while demonstrating superior computational efficiency, implementation simplicity, and methodological transparency. These results establish ImageJ as a practical, high-performance tool for glomeru- lar segmentation in renal pathology. The vali- dated workflow offers pathologists and researchers a resource-minimal, accessible alternative to com- putationally intensive DL systems, promoting scal- able adoption in clinical diagnostics and trans- lational research for objective histopathological quantification.