1,981 research outputs found
Active skeleton for bacteria modeling
The investigation of spatio-temporal dynamics of bacterial cells and their
molecular components requires automated image analysis tools to track cell
shape properties and molecular component locations inside the cells. In the
study of bacteria aging, the molecular components of interest are protein
aggregates accumulated near bacteria boundaries. This particular location makes
very ambiguous the correspondence between aggregates and cells, since computing
accurately bacteria boundaries in phase-contrast time-lapse imaging is a
challenging task. This paper proposes an active skeleton formulation for
bacteria modeling which provides several advantages: an easy computation of
shape properties (perimeter, length, thickness, orientation), an improved
boundary accuracy in noisy images, and a natural bacteria-centered coordinate
system that permits the intrinsic location of molecular components inside the
cell. Starting from an initial skeleton estimate, the medial axis of the
bacterium is obtained by minimizing an energy function which incorporates
bacteria shape constraints. Experimental results on biological images and
comparative evaluation of the performances validate the proposed approach for
modeling cigar-shaped bacteria like Escherichia coli. The Image-J plugin of the
proposed method can be found online at http://fluobactracker.inrialpes.fr.Comment: Published in Computer Methods in Biomechanics and Biomedical
Engineering: Imaging and Visualizationto appear i
Longitudinal analysis of the developing rhesus monkey brain using magnetic resonance imaging: birth to adulthood.
We have longitudinally assessed normative brain growth patterns in naturalistically reared Macaca mulatta monkeys. Postnatal to early adulthood brain development in two cohorts of rhesus monkeys was analyzed using magnetic resonance imaging. Cohort A consisted of 24 rhesus monkeys (12 male, 12 female) and cohort B of 21 monkeys (11 male, 10 female). All subjects were scanned at 1, 4, 8, 13, 26, 39, and 52 weeks; cohort A had additional scans at 156 weeks (3 years) and 260 weeks (5 years). Age-specific segmentation templates were developed for automated volumetric analyses of the T1-weighted magnetic resonance imaging scans. Trajectories of total brain size as well as cerebral and subcortical subdivisions were evaluated over this period. Total brain volume was about 64 % of adult estimates in the 1-week-old monkey. Brain volume of the male subjects was always, on average, larger than the female subjects. While brain volume generally increased between any two imaging time points, there was a transient plateau of brain growth between 26 and 39 weeks in both cohorts of monkeys. The trajectory of enlargement differed across cortical regions with the occipital cortex demonstrating the most idiosyncratic pattern of maturation and the frontal and temporal lobes showing the greatest and most protracted growth. A variety of allometric measurements were also acquired and body weight gain was most closely associated with the rate of brain growth. These findings provide a valuable baseline for the effects of fetal and early postnatal manipulations on the pattern of abnormal brain growth related to neurodevelopmental disorders
An Unsupervised Approach for Overlapping Cervical Cell Cytoplasm Segmentation
The poor contrast and the overlapping of cervical cell cytoplasm are the
major issues in the accurate segmentation of cervical cell cytoplasm. This
paper presents an automated unsupervised cytoplasm segmentation approach which
can effectively find the cytoplasm boundaries in overlapping cells. The
proposed approach first segments the cell clumps from the cervical smear image
and detects the nuclei in each cell clump. A modified Otsu method with prior
class probability is proposed for accurate segmentation of nuclei from the cell
clumps. Using distance regularized level set evolution, the contour around each
nucleus is evolved until it reaches the cytoplasm boundaries. Promising results
were obtained by experimenting on ISBI 2015 challenge dataset.Comment: 4 pages, 4 figures, Biomedical Engineering and Sciences (IECBES),
2016 IEEE EMBS Conference on. IEEE, 201
Model and Appearance Based Analysis of Neuronal Morphology from Different Microscopy Imaging Modalities
The neuronal morphology analysis is key for understanding how a brain works. This process requires the neuron imaging system with single-cell resolution; however, there is no feasible system for the human brain. Fortunately, the knowledge can be inferred from the model organism, Drosophila melanogaster, to the human system. This dissertation explores the morphology analysis of Drosophila larvae at single-cell resolution in static images and image sequences, as well as multiple microscopy imaging modalities. Our contributions are on both computational methods for morphology quantification and analysis of the influence of the anatomical aspect. We develop novel model-and-appearance-based methods for morphology quantification and illustrate their significance in three neuroscience studies.
Modeling of the structure and dynamics of neuronal circuits creates understanding about how connectivity patterns are formed within a motor circuit and determining whether the connectivity map of neurons can be deduced by estimations of neuronal morphology. To address this problem, we study both boundary-based and centerline-based approaches for neuron reconstruction in static volumes.
Neuronal mechanisms are related to the morphology dynamics; so the patterns of neuronal morphology changes are analyzed along with other aspects. In this case, the relationship between neuronal activity and morphology dynamics is explored to analyze locomotion procedures. Our tracking method models the morphology dynamics in the calcium image sequence designed for detecting neuronal activity. It follows the local-to-global design to handle calcium imaging issues and neuronal movement characteristics.
Lastly, modeling the link between structural and functional development depicts the correlation between neuron growth and protein interactions. This requires the morphology analysis of different imaging modalities. It can be solved using the part-wise volume segmentation with artificial templates, the standardized representation of neurons. Our method follows the global-to-local approach to solve both part-wise segmentation and registration across modalities.
Our methods address common issues in automated morphology analysis from extracting morphological features to tracking neurons, as well as mapping neurons across imaging modalities. The quantitative analysis delivered by our techniques enables a number of new applications and visualizations for advancing the investigation of phenomena in the nervous system
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