OpenCFU: Rapid, Accurate Colony Counting for Microbiology Labs

Automating Colony Counts with OpenCFU — Step‑by‑Step Guide

Automating colony counting saves time, reduces human error, and produces reproducible data. This guide walks you through installing OpenCFU, preparing images, optimizing settings, batch processing, and validating results so you can confidently integrate automated counts into your workflow.

What is OpenCFU

OpenCFU is an open-source image-analysis tool designed to detect and count colonies (or similar circular objects) on agar plates and other surfaces. It offers adjustable detection parameters, batch processing, and simple output formats suitable for downstream analysis.

Before you start — requirements

• A computer running Windows, macOS, or Linux.
• OpenCFU installer or build (latest stable release).
• Digital images of plates (JPEG, PNG, TIFF). Prefer high contrast, even lighting.
• Optional: basic image editor (ImageJ/Fiji or similar) for cropping/rotation.

Step 1 — Install OpenCFU

1. Download the latest release from the OpenCFU project page or GitHub releases.
2. Run the installer (Windows/macOS) or follow build instructions for Linux (compile from source if needed).
3. Launch OpenCFU to confirm successful installation.

Step 2 — Capture and prepare images

• Use a stable imaging setup: fixed camera distance, consistent lighting, neutral background.
• Aim for minimal glare and shadows; use a lightbox if available.
• Use a resolution where colonies are represented by at least several pixels across (avoid extreme downsampling).
• Save images in a lossless or high-quality format (TIFF or high-quality JPEG).
• Crop images to the plate edge so the plate fills most of the frame; rotate so plates are upright.

Step 3 — Open a sample image and set plate parameters

1. Open OpenCFU and load a representative image.
2. Use the cropping/region tool to tightly select the plate area (exclude background).
3. Set the approximate plate diameter if the software asks — this helps scale detection thresholds.
4. Choose the detection mode (standard colony detection for circular colonies).

Step 4 — Tune detection parameters

• Thresholding: Adjust the threshold to separate colonies from background. Start with automatic mode, then switch to manual if needed.
• Minimum/Maximum size: Set size bounds in pixels to exclude debris or merged colonies. Use examples from your images to estimate.
• Morphological filters: Enable options to remove edge artifacts or non-circular objects if available.
• Segmentation sensitivity: Increase to split close colonies; decrease to avoid over-segmentation.
  Iteratively adjust parameters and preview results until detected overlays match visible colonies across several representative images.

Step 5 — Validate on multiple images

• Test parameters on 5–10 images covering typical variability (different colony densities, lighting differences).
• Compare automated counts to manual counts on the same images. Calculate percent difference or error rate.
• If systematic under- or over-counting occurs, refine size thresholds and segmentation sensitivity.

Step 6 — Batch process images

1. Save your optimized settings as a preset or configuration.
2. Use OpenCFU’s batch mode to load a folder of images.
3. Select output options (per-image CSV, summary report, overlays).
4. Run the batch process and monitor for errors or failed images.

Step 7 — Post-processing and quality control

• Inspect overlay images for a random subset to ensure consistent detection.
• Flag images with unexpected counts or poor overlays for manual review.
• Merge per-image CSVs into a master spreadsheet for analysis (include filename, count, parameters used).
• Record metadata: imaging setup, date, operator, and settings used for reproducibility.

Troubleshooting common issues

• Low contrast / poor detection: improve lighting or increase threshold contrast.
• Over-segmentation (one colony split into many): lower segmentation sensitivity or increase minimum size.
• Under-segmentation (merged colonies counted as one): increase segmentation sensitivity or lower maximum size.
• Debris mistaken as colonies: tighten minimum circularity or size filters, or clean plates before imaging.

Tips for best results

• Standardize imaging (distance, lighting, background) for all plates.
• Use calibration plates with known colony counts to validate workflows regularly.
• Keep raw images and processing presets archived alongside results for auditability.
• Consider automated plate feeders or motorized imaging rigs for high-throughput needs.

Example workflow (concise)

1. Capture high-quality plate images with consistent lighting.
2. Load representative image in OpenCFU; crop to plate.
3. Tune threshold, size, and segmentation settings; save preset.
4. Validate on multiple images vs manual counts.
5. Batch process full dataset; review overlays for QC.
6. Export CSVs and compile final report.

Conclusion

OpenCFU can significantly speed up colony counting when images are captured consistently and detection parameters are carefully optimized and validated. Following this step‑by‑step guide will help you implement a reliable, reproducible automated counting workflow suitable for routine lab use.