PhD student Sam Border and his team from the CMI lab has published a new study in Scientific Reports titled “Investigating quantitative histological characteristics in renal pathology using HistoLens.” This study explores the use of HistoLens, an open-source tool developed by the lab, for the visual and quantitative analysis of histological datasets, specifically in the context of renal pathology.
HistoLens, built using MATLAB AppDesigner, provides a user-friendly graphical interface that allows researchers to analyze and compare histological differences across disease and experimental groups. The software enables the dynamic visualization of 448 hand-engineered features that quantify aspects such as color, texture, morphology, and distribution within microanatomic sub-compartments of tissue samples. These capabilities allow users to map and highlight regions within images where significant histological differences are detected.
In this study, the team demonstrated how HistoLens can identify features that correlate with key renal glomerular characteristics, effectively distinguishing between conditions such as diabetic nephropathy, amyloid nephropathy, and minimal change disease. Additionally, the tool was used to discover distinct glomerular features in the Tg26 mouse model of HIV-associated nephropathy, offering insights into the progression of renal disease in these models.
Published on July 30, 2024, the study showcases HistoLens as a versatile, off-the-shelf toolkit that can be readily applied to quantitative renal pathology research, providing a practical resource for the medical research community.