A new method to predict the development of metastases of cancer cells and accelerate treatment for patients was developed in Prof. Daphne Weihs' lab. The method works by placing cancer cells samples on gel and finding their penetrating level on it. According to the penetration level of the cell, one can predict the probability it will develop metastases. In a previous project, an algorithm was developed to automatically detect cells' locations on a DIC image (image of the cells on the gel's surface). Our first goal in this project was to improve the algorithm and reach higher accuracy. To manage this task, I added morphological operations and based on deep-learning ideas. This way the number of real cell detections improved even in challenging images. Our next goal was to segment the cells using the known cells' locations, to get information on their shapes and properties. For the segmentation we used active contours algorithm, while improving the results using ellipses that matched each cell. The ellipse detection method was based on hough transform and used as an assistance for active contours' algorithm since there was a need for a unique primer information for each cell. In this task we achieved good results and added for the segmentation an editing option for the user.
With of Weihs Mechanobiology Lab (Faculty of Bio-Medical Engineering)