DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D

Shuvaev, S. A., Lazutkin, A. A., Kedrov, A. V., Anokhin, K. V., Enikolopov, G. N., Koulakov, A. A. (2017) DALMATIAN: An Algorithm for Automatic Cell Detection and Counting in 3D. Front Neuroanat, 11. p. 117. ISSN 1662-5129 (Print)1662-5129

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URL: https://www.ncbi.nlm.nih.gov/pubmed/29311849
DOI: 10.3389/fnana.2017.00117


Current 3D imaging methods, including optical projection tomography, light-sheet microscopy, block-face imaging, and serial two photon tomography enable visualization of large samples of biological tissue. Large volumes of data obtained at high resolution require development of automatic image processing techniques, such as algorithms for automatic cell detection or, more generally, point-like object detection. Current approaches to automated cell detection suffer from difficulties originating from detection of particular cell types, cell populations of different brightness, non-uniformly stained, and overlapping cells. In this study, we present a set of algorithms for robust automatic cell detection in 3D. Our algorithms are suitable for, but not limited to, whole brain regions and individual brain sections. We used watershed procedure to split regional maxima representing overlapping cells. We developed a bootstrap Gaussian fit procedure to evaluate the statistical significance of detected cells. We compared cell detection quality of our algorithm and other software using 42 samples, representing 6 staining and imaging techniques. The results provided by our algorithm matched manual expert quantification with signal-to-noise dependent confidence, including samples with cells of different brightness, non-uniformly stained, and overlapping cells for whole brain regions and individual tissue sections. Our algorithm provided the best cell detection quality among tested free and commercial software.

Item Type: Paper
Uncontrolled Keywords: Vessels brain cell eye microscopy molecular and cellular imaging quantification and estimation segmentation
Subjects: Investigative techniques and equipment > microscopy > flourescence microscopy
Investigative techniques and equipment > imaging
Investigative techniques and equipment > optical devices > microscope techniques or equipment
CSHL Authors:
Communities: CSHL labs > Koulakov lab
Depositing User: Matt Covey
Date: 2017
Date Deposited: 12 Jan 2018 16:25
Last Modified: 12 Jan 2018 16:25
PMCID: PMC5732941
Related URLs:
URI: https://repository.cshl.edu/id/eprint/35815

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