Automated histological analysis typically involves several steps: 1. Image Acquisition: High-quality digital images of tissue samples are captured using microscopes equipped with digital cameras. 2. Preprocessing: The images are processed to enhance quality, remove noise, and correct for any distortions. 3. Segmentation: Algorithms are applied to identify and delineate different tissue structures, such as cells, nuclei, and extracellular matrix. 4. Feature Extraction: Quantitative features, such as cell size, shape, and staining intensity, are extracted from the segmented images. 5. Classification and Quantification: Machine learning models classify the tissues based on the extracted features, and quantitative metrics are calculated to provide diagnostic information.
