College of Engineering

Sima Noorani

College of Engineering

Electrical Engineering

Faculty Mentor: Dr. Andrew R. Cohen

Electrical & Computer Engineering

CNN based classification of mitotic parent stem cells in time-lapse microscopy

Time-lapse microscopy of living cells is a fundamental tool for studying human disease and development. Microscopes capture high spatiotemporal resolution images showing the dynamic behaviors of clones (family trees) of stem and cancer cells. Making sense of these images requires computer algorithms to process the data, extracting metrics of cell behaviors. One key cellular behavior that impacts all other studies is mitosis. A new model-based classifier has been developed for identifying mitotic events in live cell microscopy movies. The model-based classifier uses segmentation and tracking results to identify mitotic events with an accuracy of 78%. During the summer STAR research, a new deep learning Convolutional Neural Network (CNN) was developed to classify the distinctive pattern that the cell nuclei exhibits pre-mitosis. This new classifier was able to achieve 95% accuracy at identifying the distinctive pattern associated with mitotic parent cells. The next step will be to combine the model-based and CNN classifiers, improving the accuracy and robustness. This fall the work will be submitted to the IEEE International Symposium on Biomedical Imaging conference.