Abstract
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.
Original language | English (US) |
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Pages (from-to) | 20-33 |
Number of pages | 14 |
Journal | Cytometry Part A |
Volume | 63 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2005 |
Keywords
- Cell counting
- Confocal microscopy
- Fluorescence in situ hybridization quantification
- Hierarchical
- Image segmentation
- Model based
- Object features
- Region merging
- Three-dimensional image analysis
- Watershed segmentation
ASJC Scopus subject areas
- Pathology and Forensic Medicine
- Histology
- Cell Biology