Biomedical subjects

Kathy Olson

Publications and source records attributed to Kathy Olson.

3 recordsLinked to original sources

PubMed2005

Hierarchical, model-based merging of multiple fragments for improved three-dimensional segmentation of nuclei.

BACKGROUND: Automated segmentation of fluorescently labeled cell nuclei in three-dimensional confocal images is essential for numerous studies, e.g., spatiotemporal fluorescence in situ hybridization quantification of immediate early gene transcription. High accuracy and automation levels are required in high-throughput and large-scale studies. Common sources of segmentation error include tight clustering and fragmentation of nuclei. Previous region-based methods are limited because they perform merging of two nuclear fragments at a time. To achieve higher accuracy without sacrificing scale, more sophisticated yet computationally efficient algorithms are needed. METHODS: A recursive tree-based algorithm that can consider multiple object fragments simultaneously is described. Starting with oversegmented data, it searches efficiently for the optimal merging pattern guided by a quantitative scoring criterion based on object modeling. Computation is bounded by limiting the depth of the merging tree. RESULTS: The proposed method was found to perform consistently better, achieving merging accuracy in the range of 92% to 100% compared with our previous algorithm, which varied in the range of 75% to 97%, even with a modest merging tree depth of 3. The overall average accuracy improved from 90% to 96%, with roughly the same computational cost for a set of representative images drawn from the CA1, CA3, and parietal cortex regions of the rat hippocampus. CONCLUSION: Hierarchical tree model-based algorithms significantly improve the accuracy of automated nuclear segmentation without sacrificing speed.

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PubMed2004

3D-catFISH: a system for automated quantitative three-dimensional compartmental analysis of temporal gene transcription activity imaged by fluorescence in situ hybridization.

Fluorescence in situ hybridization (FISH) of neural activity-regulated, immediate-early gene (IEG) expression provides a method of functional brain imaging with cellular resolution. This enables the identification, in one brain, of which specific principal neurons were active during each of two distinct behavioral epochs. The unprecedented potential of this differential method for large-scale analysis of functional neural circuits is limited, however, by the time-intensive nature of manual image analysis. A comprehensive software tool for processing three-dimensional, multi-spectral confocal image stacks is described which supports the automation of this analysis. Nuclei counterstained with conventional DNA dyes and FISH signals indicating the sub-cellular distribution of specific, IEG RNA species are imaged using different spectral channels. The DNA channel data are segmented into individual nuclei by a three-dimensional multi-step algorithm that corrects for depth-dependent attenuation, non-isotropic voxels, and imaging noise. Intra-nuclear and cytoplasmic FISH signals are associated spatially with the nuclear segmentation results to generate a detailed tabular/database and graphic representation. Here we present a comprehensive validation of data generated by the automated software against manual quantification by human experts on hippocampal and parietal cortical regions (96.5% concordance with multi-expert consensus). The high degree of reliability and accuracy suggests that the software will generalize well to multiple brain areas and eventually to large-scale brain analysis.

Algorithms↗

PubMed2003

A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks.

BACKGROUND: Automated segmentation of fluorescently-labeled cell nuclei in 3D confocal microscope images is essential to many studies involving morphological and functional analysis. A common source of segmentation error is tight clustering of nuclei. There is a compelling need to minimize these errors for constructing highly automated scoring systems. METHODS: A combination of two approaches is presented. First, an improved distance transform combining intensity gradients and geometric distance is used for the watershed step. Second, an explicit mathematical model for the anatomic characteristics of cell nuclei such as size and shape measures is incorporated. This model is constructed automatically from the data. Deliberate initial over-segmentation of the image data is performed, followed by statistical model-based merging. A confidence score is computed for each detected nucleus, measuring how well the nucleus fits the model. This is used in combination with the intensity gradient to control the merge decisions. RESULTS: Experimental validation on a set of rodent brain cell images showed 97% concordance with the human observer and significant improvement over prior methods. CONCLUSIONS: Combining a gradient-weighted distance transform with a richer morphometric model significantly improves the accuracy of automated segmentation and FISH analysis.

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