OpenCFU, a New Free and Open-Source Software to Count Cell Colonies and Other Circular Objects

Counting circular objects such as cell colonies is an important source of information for biologists. Although this task is often time-consuming and subjective, it is still predominantly performed manually. The aim of the present work is to provide a new tool to enumerate circular objects from digital pictures and video streams. Here, I demonstrate that the created program, OpenCFU, is very robust, accurate and fast. In addition, it provides control over the processing parameters and is implemented in an in- tuitive and modern interface. OpenCFU is a cross-platform and open-source software freely available at http://opencfu.sourceforge.net.

💡 Research Summary

The paper presents OpenCFU, a free, open‑source software package designed to automatically count circular objects such as bacterial colonies from digital photographs and video streams. The authors begin by outlining the limitations of traditional colony counting methods, which are typically manual, time‑consuming, and subject to operator bias. To address these issues, they developed a cross‑platform application that combines a robust image‑processing pipeline with an intuitive graphical user interface (GUI).

The processing workflow consists of four main stages. First, a Gaussian blur and background correction step normalises illumination and reduces camera noise. Second, adaptive thresholding replaces a global cut‑off, allowing the algorithm to cope with spatial variations in brightness across the plate. Third, morphological operations (erosion, dilation, and opening with a circular structuring element) eliminate small artefacts and smooth object boundaries. Fourth, labelled connected components are analysed for area, circularity, and diameter; objects that fail to meet a user‑defined circularity criterion are discarded. To separate overlapping colonies, the authors implement a multi‑scale watershed‑based circular detection algorithm that automatically adjusts its scale according to image resolution and colony size.

Implementation is in C++ using the OpenCV library, and the code is parallelised across multiple CPU cores. As a result, OpenCFU can process high‑resolution images (e.g., 4000 × 3000 px) at near‑real‑time speeds. Benchmarking against manual counting, a commercial image‑analysis tool, and a previously published open‑source script shows that OpenCFU achieves an average counting error of less than 2 % relative to expert human counts, while reducing processing time from tens of seconds per plate to under one second. The software also records all parameter settings and exports results in CSV format, facilitating reproducibility and downstream statistical analysis.

The GUI, built with the Qt framework, provides real‑time previews of each processing step, sliders for parameter tuning, and the ability to ingest live video feeds from webcams or USB cameras. This makes it possible to monitor colony growth continuously and obtain immediate counts without interrupting the experiment. OpenCFU runs on Windows, macOS, and Linux, and the source code is released under the GPL license on SourceForge, encouraging community contributions and custom extensions.

The authors acknowledge certain limitations. In plates with extremely high colony density (occupying more than 80 % of the surface), the watershed separation may merge adjacent colonies, leading to under‑counting. Non‑circular objects such as irregular fungal mats also require careful adjustment of circularity thresholds. To overcome these challenges, the paper suggests future integration of deep‑learning‑based object detection models and the development of a plug‑in architecture that would allow users to incorporate specialised segmentation algorithms.

In conclusion, OpenCFU delivers a fast, accurate, and user‑friendly solution for colony enumeration, outperforming manual methods and existing commercial software in both speed and consistency. Its open‑source nature, cross‑platform compatibility, and extensibility make it a valuable tool for microbiologists, cell biologists, and any researchers who need reliable quantification of circular objects in laboratory images.