A New Unsupervised Approach for Segmenting and Counting Cells in High-Throughput Microscopy Image Sets

Abstract

New technological advances in automated mi- croscopy have given rise to large volumes of data, which have made human-based analysis infeasible, heightening the need for automatic systems for high-throughput microscopy applications. In particular, in the field of fluorescence microscopy, automatic tools for image analysis are making an essential contribution in order to increase the statistical power of the cell analysis process. The development of these automatic systems is a difficult task due to both the diversification of the staining patterns and the local variability of the images. In this paper, we present an unsupervised approach for automatic cell segmentation and counting, namely CSC, in high-throughput microscopy images. The segmentation is performed by dividing the whole image into square patches that undergo a gray level clustering followed by an adaptive thresholding. Subsequently, the cell labeling is obtained by detecting the centers of the cells, using both distance transform and curvature analysis, and by applying a region growing process. The advantages of CSC are manifold. The foreground detection process works on gray levels rather than on individual pixels, so it proves to be very efficient. Moreover, the combination of distance transform and curvature analysis makes the counting process very robust to clustered cells. A further strength of the CSC method is the limited number of parameters that must be tuned. Indeed, two different versions of the method have been considered, CSC-7 and CSC-3, depending on the number of parameters to be tuned. The CSC method has been tested on several publicly available image datasets of real and synthetic images. Results in terms of standard metrics and spatially-aware measures show that CSC outperforms the current state of the art techniques.

Critical Review of Processing and Classification Techniques for Images and Spectra in Microplastic Research

“…147 However, these methods often encounter problems with the classification of irregular object patterns and noise. 152 Additionally, some of these algorithms rely on hand-crafted feature selection or shape assumptions, 150,151 which often limit their use for the detection of well-defined objects with a specific feature within the context of other objects that give rise to contrast in an image.…”

Applying fluorescence in situ hybridization to aquatic systems with cyanobacteria blooms: Autofluorescence suppression and high‐throughput image analysis

“…2b). Dynamic thresholding, as suggested by Riccio et al (2018), was then applied using the “weighted mean local pixel neighborhood” method (Fig. 2c).…”

Synthetic Image Rendering Solves Annotation Problem in Deep Learning Nanoparticle Segmentation

“…They can be found in products ranging from cosmetics, textiles, and foods, Several approaches [11][12][13][14][15][16][17][18][19][20][21] have been proposed for automated image analysis of SEM and TEM images. However, most of these approaches rely on single thresholds for the feature separation, [20,21] encounter major difficulties caused by irregular object patterns and noise, [22] or they rely on hand-crafted features for the particle shapes, [14,15] which impair the generalization potential of such algorithms for the characterization of arbitrary nanoparticles or heterogeneous nanoparticle ensembles. In order to handle the complexity of nanoparticle images that include various sizes, shapes, distributions, and shadow variations more sophisticated image analysis approaches are required.…”