Karin Althoff

Combined segmentation and tracking of neural stem-cells

In this paper we analyze neural stem/progenitor cells in an time-lapse image sequence. By using information about the previous positions of the cells, we are able to make a better selection of possible cells out of a collection of blob-like objects. As a blob detector we use Laplacian of Gaussian (LoG) filters at multiple scales, and the cell contours of the selected cells are segmented using dynamic programming. After the segmentation process the cells are tracked in the sequence using a combined nearest-neighbor and correlation matching technique. An evaluation of the system show that 95% of the cells were correctly segmented and tracked between consecutive frames.

An automatic system for in vitro cell migration studies

This paper describes a system for in vitro cell migration analysis. Adult neural stem/progenitor cells are studied using time‐lapse bright‐field microscopy and thereafter stained immunohistochemically to find and distinguish undifferentiated glial progenitor cells and cells having differentiated into type‐1 or type‐2 astrocytes. The cells are automatically segmented and tracked through the time‐lapse sequence. An extension to the Chan‐Vese Level Set segmentation algorithm, including two new terms for specialized growing and pruning, made it possible to resolve clustered cells, and reduced the tracking error by 65%. We used a custom‐built manual correction module to form a ground truth used as a reference for tracked cells that could be identified from the fluorescence staining. On average, the tracks were correct 95% of the time, using our new segmentation. The tracking, or association of segmented cells, was performed using a 2‐state Hidden Markov Model describing the random behaviour of the cells. By re‐estimating the motion model to conform with the segmented data we managed to reduce the number of tracking parameters to essentially only one. Upon characterization of the cell migration by the HMM state occupation function, it was found that glial progenitor cells were moving randomly 2/3 of the time, while the type‐2 astrocytes showed a directed movement 2/3 of the time. This finding indicates possibilities for cell‐type specific identification and cell sorting of live cells based on specific movement patterns in individual cell populations, which would have valuable applications in neurobiological research.

Tracking contrast in echocardiography by a combined snake and optical flow technique

Contrast-echocardiography in conjunction with real-time video-densiometry can be an effective means of studying right ventricular (RV) structural changes, e.g. in patients diagnosed with arrhythmogenic right ventricular dysplasia (ARVD). In order to characterize RV flow pattern it may be necessary to track the front of the contrast agent as it enters the RV. The active contour model (ACM) is a standard image analysis method, which can be applied to the time-dynamic tracking problem. To improve tracking speed we extended the formulation of ACM by including an additional force, derived from the optical flow field, another standard image analysis algorithm. This reduced the number of iterations needed to find the front of the contrast agent significantly. Also the changes in intensity of the contrast agent over time were studied. Two groups were compared, one with 30 patients diagnosed with ARVD and one with 18 healthy volunteers. Our study shows that that using our suggested method (calculating wash-in and wash-out time indices) it is possible to discriminate between the two groups.

Time-lapse microscopy and image processing for stem cell research modeling cell migration

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.

Tracking neural stem cells in time-lapse microscopy image sequences

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.

Time-Lapse Microscopy and Classification of in Vitro Cell Migration Using Hidden Markov Modeling

This paper describes a system for in vitro cell migration analysis. Adult neural stem/progenitor cells are studied using time-lapse microscopy and thereafter stained immunohistochemically to find and distinguish between undifferentiated glial progenitor cells and cells having differentiated into type-1 or type-2 astrocytes. The cells are automatically segmented and tracked throughout the time-lapse sequence. The evaluation showed that 88% of the cells were correctly segmented and tracked by the automatic system. Upon characterization of the cell migration by hidden Markov modeling, it was found that the motion of glial progenitor cells was random 2/3 of the time, while the type-2 astrocytes showed a directed movement 2/3 of the time. This finding indicates possibilities for cell-type specific identification and cell sorting of live cells based on specific movement patterns in individual cell populations, which will have valuable applications in neurobiological research