TAMMiCol: Tool for analysis of the morphology of microbial colonies

Abstract

Many microbes are studied by examining colony morphology via two-dimensional top-down images. The quantification of such images typically requires each pixel to be labelled as belonging to either the colony or background, producing a binary image. While this may be achieved manually for a single colony, this process is infeasible for large datasets containing thousands of images. The software Tool for Analysis of the Morphology of Microbial Colonies (TAMMiCol) has been developed to efficiently and automatically convert colony images to binary. TAMMiCol exploits the structure of the images to choose a thresholding tolerance and produce a binary image of the colony. The images produced are shown to compare favourably with images processed manually, while TAMMiCol is shown to outperform standard segmentation methods. Multiple images may be imported together for batch processing, while the binary data may be exported as a CSV or MATLAB MAT file for quantification, or analysed using statistics built into the software. Using the in-built statistics, it is found that images produced by TAMMiCol yield values close to those computed from binary images processed manually. Analysis of a new large dataset using TAMMiCol shows that colonies of Saccharomyces cerevisiae reach a maximum level of filamentous growth once the concentration of ammonium sulfate is reduced to 200 μM. TAMMiCol is accessed through a graphical user interface, making it easy to use for those without specialist knowledge of image processing, statistical methods or coding.

HYPHAEdelity: a quantitative image analysis tool for assessing peripheral whole colony filamentation

“…A MacBook Pro laptop running OSX 11.1 with a 2.7 GHz Quad-Core Intel Core i7 processor takes approximately 20 seconds to transform high-resolution yeast colonies images to binary images, with identified edges and filamentous growth identified, and subsequently calculates the area and standardized f-measure of filamentous growth from the colony. The assessment of a single image manually could take 15 minutes, while on average, the analysis completed with HYPHAEdelity takes about 20 seconds per image to evaluate, which is also superior to other currently available software that may take several hours to analyze 10 images (Tronnolone et al 2018 ).…”

ColFeatures: Automated data extraction and classification of bacterial colonies

“…Counting the number of colonies is relatively simple to automate, and several algorithms have been published (9)(10)(11)(12)(13)(14)(15) and some are included in the software for commercial equipment (16). Image segmentation methods are diverse and many different approaches have been implemented.…”

A deep learning approach to capture the essence of Candida albicans morphologies

“…There have been several earlier efforts in fungal image analysis 25 including approaches to computationally differentiate between types of fungi and allergenic fungal spores 26 , and to characterize the macro-structure of mycelium or filamentous growth [27][28][29][30][31][32][33][34] . Tleis and Verbeek experimented with a suite of machine learning techniques to segment S. cerevisiae cells and measured a range of features and textures from two channel images acquired by a laser scanning confocal microscope 35 .…”