Abstract: A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides.

Abstract: A facility includes systems and methods for providing a learning-based image analysis approach for the automated detection, classification, and counting of objects (e.g., cell nuclei) within digitized pathology tissue slides. The facility trains an object classifier using a plurality of reference sample slides. Subsequently, and in response to receiving a scanned image of a slide containing tissue data, the facility separates the whole slide into a background region and a tissue region using image segmentation techniques. The facility identifies dominant color regions within the tissue data and identifies seed points within those regions using, for example, a radial symmetry based approach. Based at least in part on those seed points, the facility generates a tessellation, each distinct area in the tessellation corresponding to a distinct detected object. These objects are then classified using the previously-trained classifier. The facility uses the classified objects to score slides.