Proc. SPIE. 5747, Medical Imaging 2005: Image Processing
KEYWORDS: Image processing algorithms and systems, Detection and tracking algorithms, Image segmentation, Computer programming, Medical imaging, Stem cells, Neurons, Time lapse microscopy, Neurogenesis, Automatic tracking
This paper describes an algorithm for tracking neural stem/progenitor cells in a time-lapse microscopy image sequence. The cells were segmented in a semiautomatic way using dynamic programming. Since the interesting cells were identified by fluorescent staining at the end of the sequence, the tracking was performed backwards. The number of detected cells varied throughout the sequence: cells could appear or disappear at the image boundaries or at cell clusters, some cells split, and the segmentation was not always correct. To solve this asymmetric assignment problem, a modified version of the auction algorithm by Bertsekas was used. The assignment weights were calculated based on distance, correlation and size between possible matching cells. Cell splits are of special interest, therefore tracks without a matching cell were divided into two groups: 1. Merging cells (splitting cells, moving forward in time) and 2. Non-merging cells. These groups were separated based on difference in size of the involved cells, and difference in image intensity of the contour and interior of the possibly merged cell. The tracking algorithm was evaluated using a sequence consisting of 57 images, each image containing approximately 50 cells. The evaluation showed that 99% of the cell-to-cell associations were correct. In most cases, only one association per track was incorrect so in total 55 out of 78 different tracks in the sequence were tracked correctly. Further improvements will be to apply interleaved segmentation and tracking to produce a more reliable segmentation as well as better tracking results.
This paper presents hardware and software procedures for automated cell tracking and migration modeling. A time-lapse microscopy system equipped with a computer controllable motorized stage was developed. The performance of this stage was improved by incorporating software algorithms for stage motion displacement compensation and auto focus. The microscope is suitable for in-vitro stem cell studies and allows for multiple cell culture image sequence acquisition. This enables comparative studies concerning rate of cell splits, average cell motion velocity, cell motion as a function of cell sample density and many more. Several cell segmentation procedures are described as well as a cell tracking algorithm. Statistical methods for describing cell migration patterns are presented. In particular, the Hidden Markov Model (HMM) was investigated. Results indicate that if the cell motion can be described as a non-stationary stochastic process, then the HMM can adequately model aspects of its dynamic behavior.