The specific goal of our research is to develop automated methods for quantitative analysis of tumor cells from microscopic images. By segmenting living tumor cells, cell behavior under stress can be studied. Therefore, accurate acquisition and analysis of microscope images from living cell cultures are of utmost importance. If cell behavior can be studied through their life span, cell motility and shape changes can be quantified and analyzed in relation with the severity of induced stress. Consequently, cell responses to the environment can be quantitatively analyzed. The Large Scale Digital Cell Analysis System developed at the University of Iowa provides a capability for real-time cell image acquisition. In the work presented here, feasibility of fully automated living tumor cell segmentation is demonstrated allowing future quantitative cell studies. An automated method for identification of the cell boundaries in microscopy images is presented.
This work describes a method for detecting mitotic cells in time-lapse microscopy images of live cells. The image sequences are from the Large Scale Digital Cell Analysis System (LSDCAS) at the University of Iowa. LSDCAS is an automated microscope system capable of monitoring 1000 microscope fields over time intervals of up to one month. Manual analysis of the image sequences can be extremely time consuming. This work is part of a larger project to automate the image sequence analysis. A three-step approach is used. In the first step, potential mitotic cells are located in the image sequences. In the second step, object border segmentation is performed with the watershed algorithm. Objects in adjacent frames are grouped into object sequences for classification. In the third step, the image sequences are converted to feature vector sequences. The feature vectors contain spatial and temporal information. Hidden Markov Models (HMMs) are used to classify the feature vector sequences into dead cells, cell edges, and dividing cells. Discrete and continuous HMMs were trained on 500 sequences. The discrete HMM recognition rates were 62% for dead cells, 77% for cell edges, and 75% for dividing cells. The continuous HMM results were 68%, 88% and 77%.