Abstract
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.
Highlights
We focused on the following specific problems with regard to cell detection (Figure 1):
Since 1960s, when the first systems were developed to automate cell detection in images (Meijering, 2012), a number of cell detection algorithms has been published
There is a demand for a cell detection algorithm generic enough to be adjustable for a wide range of applications (Schmitz et al, 2014)
Summary
GROWING evidence suggests that counts of various cell types identified by gene expression, internal and external markers correlate with various important factors, including central nervous system activity (Gage et al, 2008), impact of drugs of abuse (Eisch and Harburg, 2006), disease (Jin et al, 2004; Geraerts et al, 2007), aging (Jin et al, 2003), and other conditions (Cameron et al, 1998; Gao et al, 2009; Torner et al, 2009; Tanti et al, 2012). The majority of studies are conducted using two-dimensional tissue section techniques (Peterson, 2004; Schmitz and Hof, 2005). This traditional approach, combined with planar microscopy techniques, has numerous drawbacks, including low throughput capacity (Howard and Reed, 2010), loss of data due to interpolation (West, 2012), and difficulty in recovering 3D information. Instead of the full information about cell positions in the sample, only average cell counts may be calculated Such estimates may lead to incorrect values, biases, and, to inaccurate results (West, 2012)
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