Quantitative Digital Image Analysis

Quantitative digital image analysis in preclinical research involves employing advanced computational techniques to extract quantitative data from images captured during experiments or studies. This process entails utilizing specialized software and algorithms to quantify various parameters such as size, shape, intensity, and spatial distribution of cells or features within digitized images of tissue sections.

By converting visual information into measurable data points, we can perform rigorous and reproducible analysis, enabling in-depth investigations of biological phenomena, disease progression, and treatment efficacy. This approach facilitates the generation of quantitative data, enhancing the accuracy, objectivity, and efficiency of preclinical studies.

Quantitative analysis of digitized IHC and IF sections is a core strength of Biospective. We have developed a unique technology platform that we call PERMITS^™. We have developed fully-automated segmentation algorithms which quantify the staining and can generate various morphological measures, like microglial activation.

A unique aspect of PERMITS^™ is that we analyze the IHC or IF data over entire regions-of-interest. We use deep-learning artificial intelligence (AI) algorithms that we have developed to label the neuroanatomy on tissue sections. By analyzing an entire brain region, such as the striatum, hippocampus, or amygdala, we can gain statistical power compared with simple analysis of random high-magnification snapshots.