This paper describes a computational model for image formation of in-vitro adult hippocampal progenitor (AHP)
cells, in bright-field time-lapse microscopy. Although this microscopymodality barely generates sufficient contrast
for imaging translucent cells, we show that by using a stack of defocused image slices it is possible to extract
position and shape of spherically shaped specimens, such as the AHP cells. This inverse problem was solved
by modeling the physical objects and image formation system, and using an iterative nonlinear optimization
algorithm to minimize the difference between the reconstructed and measured image stack. By assuming that
the position and shape of the cells do not change significantly between two time instances, we can optimize
these parameters using the previous time instance in a Bayesian estimation approach. The 3D reconstruction
algorithm settings, such as focal sampling distance, and PSF, were calibrated using latex spheres of known size
and refractive index. By using the residual between reconstructed and measured image intensities, we computed
a peak signal-to-noise ratio (PSNR) to 28 dB for the sphere stack. A biological specimen analysis was done using
an AHP cell, where reconstruction PSNR was 28 dB as well. The cell was immuno-histochemically stained and
scanned in a confocal microscope, in order to compare our cell model to a ground truth. After convergence the
modelled cell volume had an error of less than one percent.
KEYWORDS: Image segmentation, Detection and tracking algorithms, Time lapse microscopy, Automatic tracking, Image processing algorithms and systems, Stem cells, Computer programming, Medical imaging, Neurogenesis, Neurons
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.
Different shape representation and classification methods for complex medical lesions were compared using oral lesions as a case study. The problem studied was the discrimination between potentially cancerous lesions, called leukoplakia, and other usually harmless lesions, called lichenoid reactions, which can appear in human oral cavities. The classification problem is difficult because these lesions vary in shape within classes and there are no easily recognizable characteristics. The representations evaluated were the centroidal profile function, the curvature function, and polar and complex coordinate functions. From these representations, translation, scale and rotation independent features were derived using Fourier transformations, auto-regressive modeling, and Zernike moments. A nonparametric kNN classifier with the leave-one-out cross-validation method was used as a classifier. An overall classification accuracy of about 84% was achieved using only the shape properties of the lesions, compared with a human visual classification rate of 65%. The best results were obtained using complex representation and Fourier/Zernike methods. In clinical practice, the preliminary diagnosis is based mainly on the visual inspection of the oral cavity, using both color, shape and texture as differentiating parameters. This study showed that machine analysis of shape could also play an important part in diagnosis and decisions regarding future treatment.
A shape-based classification method is developed based upon the Generalized Fourier Representation (GFR). GFR can be regarded as an extension of traditional polar Fourier descriptors, suitable for description of closed objects, both convex and concave, with or without holes. Explicit relations of GFR coefficients to regular moments, moment invariants and affine moment invariants are given in the paper. The dual linear relation between GFR coefficients and regular moments was used to compare shape features derive from GFR descriptors and Hu's moment invariants. the GFR was then applied to a clinical problem within oral medicine and used to represent the contours of the lesions in the oral cavity. The lesions studied were leukoplakia and different forms of lichenoid reactions. Shape features were extracted from GFR coefficients in order to classify potentially cancerous oral lesions. Alternative classifiers were investigated based on a multilayer perceptron with different architectures and extensions. The overall classification accuracy for recognition of potentially cancerous oral lesions when using neural network classifier was 85%, while the classification between leukoplakia and reticular lichenoid reactions gave 96% (5-fold cross-validated) recognition rate.
The aim of the study was to investigate effective image analysis methods for the discrimination of two oral lesions, oral lichenoid reactions and oral leukoplakia, using only color information. Five different color representations (RGB, Irg, HSI, I1I2I3 and La*b*) were studied and their use for color analysis of mucosal images evaluated. Four common classifiers (Fisher's linear discriminant, Gaussian quadratic, kNN-Nearest Neighbor and Multilayer Perceptron) were chosen for the evaluation of classification performance. The feature vector consisted of the mean color difference between abnormal and normal regions extracted from digital color images. Classification accuracy was estimated using resubstitution and 5-fold crossvalidation methods. The best classification results were achieved in HSI color system and using linear discriminant function. In total, 70 out of 74 (94.6%) lichenoid reactions and 14 out of 20 (70.0%) of leukoplakia were correctly classified using only color information.
We have studied three-dimensional reconstruction methods to estimate the cell volume of astroglial cells in primary culture. The studies are based on fluorescence imaging and optical sectioning. An automated image-acquisition system was developed to collect two-dimensional microscopic images. Images were reconstructed by the Linear Maximum a Posteriori method and the non-linear Maximum Likelihood Expectation Maximization (ML-EM) method. In addition, because of the high computational demand of the ML-EM algorithm, we have developed a fast variant of this method. (1) Advanced image analysis techniques were applied for accurate and automated cell volume determination. (2) The sensitivity and accuracy of the reconstruction methods were evaluated by using fluorescent micro-beads with known diameter. The algorithms were applied to fura-2-labeled astroglial cells in primary culture exposed to hypo- or hyper-osmotic stress. The results showed that the ML-EM reconstructed images are adequate for the determination of volume changes in cells or parts thereof.
Cell volume changes are often associated with important physiological and pathological processes in the cell. These changes may be the means by which the cell interacts with its surrounding. Astroglial cells change their volume and shape under several circumstances that affect the central nervous system. Following an incidence of brain damage, such as a stroke or a traumatic brain injury, one of the first events seen is swelling of the astroglial cells. In order to study this and other similar phenomena, it is desirable to develop technical instrumentation and analysis methods capable of detecting and characterizing dynamic cell shape changes in a quantitative and robust way. We have developed a technique to monitor and to quantify the spatial and temporal volume changes in a single cell in primary culture. The technique is based on two- and three-dimensional fluorescence imaging. The temporal information is obtained from a sequence of microscope images, which are analyzed in real time. The spatial data is collected in a sequence of images from the microscope, which is automatically focused up and down through the specimen. The analysis of spatial data is performed off-line and consists of photobleaching compensation, focus restoration, filtering, segmentation and spatial volume estimation.
We report the results of our femtosecond laser study of the photophysical and photochemical properties of the DCM styrenic dye. Our femtosecond pump-probe experiments using a white light continuum show a red shift of the gain spectrum and a blue shift of the S1 yields Sn absorption band due to a fast reorganization of the solvent cage around the highly polar fluorescent first singlet excited state. There is no evidence of any locally excited (LE) to twisted intramolecular charge transfer (TICT) state transition.
We describe a method for automatic realignment of consecutive 2-D microscopic images of brain cortex. The procedure is capable of carrying out high-quality realignment of 10 - 20 images per hour. The resulting image stack can be viewed in real-time by cinematographic animation or used for 3-D object reconstruction. The technique does not rely on expensive hardware, but can be implemented on low-cost PCs and workstations.
A program package is described that: (1) handles a stack of several thousand aligned sequential photographic 2-D images as stored in an image processing system, (2) builds a database from information extracted from objects present in the stack of 2-D images, (3) transfers the database to an advanced graphic terminal, (4) reconstructs a 3-D object space, and (5) allows on-line interaction between the image processing system and the graphic terminal. The cell content of a prism of motor cerebral cortex of the cat is reconstructed as an application sample.
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