Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention 12/2010; 13(Pt 3):634-41. DOI: 10.1007/978-3-642-15711-0_79
We present a novel approach for extracting cluttered objects based on their morphological properties. Specifically, we address the problem of untangling Caenorhabditis elegans clusters in high-throughput screening experiments. We represent the skeleton of each worm cluster by a sparse directed graph whose vertices and edges correspond to worm segments and their adjacencies, respectively. We then search for paths in the graph that are most likely to represent worms while minimizing overlap. The worm likelihood measure is defined on a low-dimensional feature space that captures different worm poses, obtained from a training set of isolated worms. We test the algorithm on 236 microscopy images, each containing 15 C. elegans worms, and demonstrate successful cluster untangling and high worm detection accuracy.
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"It is also relatively easy to incorporate continuous object-level regularization into level sets, such as shape priors. Another type of energy-based model is based on graph search [16,17], graph cuts [18,19] or normalized cuts . Such methods attempt to derive the segmentation with global constraints, using well-defined graphical structures to represent the spatial relationships between regions. "
[Show abstract][Hide abstract] ABSTRACT: Background
Segmenting cell nuclei in microscopic images has become one of the most important routines in modern biological applications. With the vast amount of data, automatic localization, i.e. detection and segmentation, of cell nuclei is highly desirable compared to time-consuming manual processes. However, automated segmentation is challenging due to large intensity inhomogeneities in the cell nuclei and the background.
We present a new method for automated progressive localization of cell nuclei using data-adaptive models that can better handle the inhomogeneity problem. We perform localization in a three-stage approach: first identify all interest regions with contrast-enhanced salient region detection, then process the clusters to identify true cell nuclei with probability estimation via feature-distance profiles of reference regions, and finally refine the contours of detected regions with regional contrast-based graphical model. The proposed region-based progressive localization (RPL) method is evaluated on three different datasets, with the first two containing grayscale images, and the third one comprising of color images with cytoplasm in addition to cell nuclei. We demonstrate performance improvement over the state-of-the-art. For example, compared to the second best approach, on the first dataset, our method achieves 2.8 and 3.7 reduction in Hausdorff distance and false negatives; on the second dataset that has larger intensity inhomogeneity, our method achieves 5% increase in Dice coefficient and Rand index; on the third dataset, our method achieves 4% increase in object-level accuracy.
To tackle the intensity inhomogeneities in cell nuclei and background, a region-based progressive localization method is proposed for cell nuclei localization in fluorescence microscopy images. The RPL method is demonstrated highly effective on three different public datasets, with on average 3.5% and 7% improvement of region- and contour-based segmentation performance over the state-of-the-art.
"For example, the current implementation of WormScan requires non-overlapping nematodes for effective image analysis, which is achieved by limiting worm density to less than 150 individuals on a 5.5 cm diameter plate. However, worms can be digitally untangled, which could allow a higher density of worms per plate to be analyzed in future studies . Furthermore, ImageJ was adapted to allow WormScan to observe similar sized species. "
[Show abstract][Hide abstract] ABSTRACT: There are four main phenotypes that are assessed in whole organism studies of Caenorhabditis elegans; mortality, movement, fecundity and size. Procedures have been developed that focus on the digital analysis of some, but not all of these phenotypes and may be limited by expense and limited throughput. We have developed WormScan, an automated image acquisition system that allows quantitative analysis of each of these four phenotypes on standard NGM plates seeded with E. coli. This system is very easy to implement and has the capacity to be used in high-throughput analysis.
Our system employs a readily available consumer grade flatbed scanner. The method uses light stimulus from the scanner rather than physical stimulus to induce movement. With two sequential scans it is possible to quantify the induced phototactic response. To demonstrate the utility of the method, we measured the phenotypic response of C. elegans to phosphine gas exposure. We found that stimulation of movement by the light of the scanner was equivalent to physical stimulation for the determination of mortality. WormScan also provided a quantitative assessment of health for the survivors. Habituation from light stimulation of continuous scans was similar to habituation caused by physical stimulus.
There are existing systems for the automated phenotypic data collection of C. elegans. The specific advantages of our method over existing systems are high-throughput assessment of a greater range of phenotypic endpoints including determination of mortality and quantification of the mobility of survivors. Our system is also inexpensive and very easy to implement. Even though we have focused on demonstrating the usefulness of WormScan in toxicology, it can be used in a wide range of additional C. elegans studies including lifespan determination, development, pathology and behavior. Moreover, we have even adapted the method to study other species of similar dimensions.
PLoS ONE 03/2012; 7(3):e33483. DOI:10.1371/journal.pone.0033483 · 3.23 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Major challenges remain in the extraction of rich information from high-throughput microscopy experiments. In this paper, I describe some of these challenges, particularly those that are the subject of ongoing research in my laboratory. The challenges include segmenting neurons, co-cultures of different cell types, and whole organisms; segmenting and tracking cells in time-lapse images; quantifying complex phenotypic changes; and discovering biologically relevant subpopulations of cells.
Proceedings of the 8th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2011, March 30 - April 2, 2011, Chicago, Illinois, USA; 01/2011