Novel image analysis approach quantifies morphological characteristics of 3D breast culture acini with varying metastatic potentials.

Prognosis of breast cancer is primarily predicted by the histological grading of the tumor, where pathologists manually evaluate microscopic characteristics of the tissue. This labor intensive process suffers from intra- and inter-observer variations; thus, computer-aided systems that accomplish this assessment automatically are in high demand. We address this by developing an image analysis framework for the automated grading of breast cancer in in vitro three-dimensional breast epithelial acini through the characterization of acinar structure morphology.

Quantitative metric profiles capture three-dimensional temporospatial architecture to discriminate cellular functional states.

Background: Computational analysis of tissue structure reveals sub-visual differences in tissue functional states by extracting quantitative signature features that establish a diagnostic profile. Incomplete and/or inaccurate profiles contribute to misdiagnosis.

Methods: In order to create more complete tissue structure profiles, we adapted our cell-graph method for extracting quantitative features from histopathology images to now capture temporospatial traits of three-dimensional collagen hydrogel cell cultures.