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Proceedings Paper

Iterative approach to joint segmentation of cellular structures
Author(s): Peter Ajemba; Richard Scott; Janakiramanan Ramachandran; Qiuhua Liu; Faisal Khan; Jack Zeineh; Michael Donovan; Gerardo Fernandez
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Paper Abstract

Accurate segmentation of overlapping nuclei is essential in determining nuclei count and evaluating the sub-cellular localization of protein biomarkers in image Cytometry and Histology. Current cellular segmentation algorithms generally lack fast and reliable methods for disambiguating clumped nuclei. In immuno-fluorescence segmentation, solutions to challenges including nuclei misclassification, irregular boundaries, and under-segmentation require reliable separation of clumped nuclei. This paper presents a fast and accurate algorithm for joint segmentation of cellular cytoplasm and nuclei incorporating procedures for reliably separating overlapping nuclei. The algorithm utilizes a combination of ideas and is a significant improvement on state-of-the-art algorithms for this application. First, an adaptive process that includes top-hat filtering, blob detection and distance transforms estimates the inverse illumination field and corrects for intensity non-uniformity. Minimum-error-thresholding based binarization augmented by statistical stability estimation is applied prior to seed-detection constrained by a distance-map-based scale-selection to identify candidate seeds for nuclei segmentation. The nuclei clustering step also incorporates error estimation based on statistical stability. This enables the algorithm to perform localized error correction. Final steps include artifact removal and reclassification of nuclei objects near the cytoplasm boundary as epithelial or stroma. Evaluation using 48 realistic phantom images with known ground-truth shows overall segmentation accuracy exceeding 96%. It significantly outperformed two state-of-the-art algorithms in clumped nuclei separation. Tests on 926 prostate biopsy images (326 patients) show that the segmentation improvement improves the predictive power of nuclei architecture features based on the minimum spanning tree algorithm. The algorithm has been deployed in a large scale pathology application.

Paper Details

Date Published: 14 February 2012
PDF: 6 pages
Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83140X (14 February 2012); doi: 10.1117/12.911319
Show Author Affiliations
Peter Ajemba, Aureon Biosciences, Inc. (United States)
Richard Scott, Aureon Biosciences, Inc. (United States)
Janakiramanan Ramachandran, Aureon Biosciences, Inc. (United States)
Qiuhua Liu, Aureon Biosciences, Inc. (United States)
Faisal Khan, Aureon Biosciences, Inc. (United States)
Jack Zeineh, Aureon Biosciences, Inc. (United States)
Michael Donovan, Aureon Biosciences, Inc. (United States)
Gerardo Fernandez, Aureon Biosciences, Inc. (United States)

Published in SPIE Proceedings Vol. 8314:
Medical Imaging 2012: Image Processing
David R. Haynor; Sébastien Ourselin, Editor(s)

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