Figure 8: A flow chart of the proposed quantitative analysis approach in measuring morphological-based features of acini. First, fluorescently labeled images of the cultures are captured by 3D confocal microscopy. Next, individual acinar structures in these images are segmented and labeled. We then extract the proposed features based on the acini morphology that lead to statistically significant unique feature profiles between functionally different stages of cancer. Finally, automated grading of cancer is achieved by supervised machine learning.