Script to evaluate segmentation results

The following segmentation statistics are available:

• DICE
• AJI
• AJI+ (another variant of AJI)
• PQ
• Boundary F1 score

The following instance detection statistics are available:

• Recall - for the threshold IoU=0.5
• Precision - for the threshold IoU=0.5
• F1 - for the threshold IoU=0.5
• FDR - for the threshold IoU=0.5

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Requirements

To install all the required dependencies, use the provided requirements.txt file:

pip install -r requirements.txt

How to Use the Script

1. Prepare Your Data

You will need:

• Prediction masks saved in one folder.
• Ground truth (GT) masks saved in another folder.

File requirements:

• The filenames of corresponding masks in both folders should match exactly. For example: mask_1.npy in both GT and prediction folders.
• The masks must be saved as NumPy arrays (.npy files).
• Each nucleus in the mask must have a unique ID. So if you have two nuclei in the mask, one will be filled with values 1 and the second with values 2. The background should be filled with 0.

2. Configure the Script

• Open the file: src/calculate_stats.py.
• Set the paths to your directories containing the prediction masks and ground truth masks.

3. Execute the script:

python src/calculate_stats.py

• The script will process the data and save results, including plots and evaluation metrics. More details about the output files can be found in the comments within calculate_stats.py.

4. Results

When the script is executed, three new folders will be created in the main results folder:

1. stats folder

This folder contains the following files:

• bf_vis/
  A subfolder containing visualizations of the Boundary F1 score for each image.
• stats.csv
  A CSV file with statistics calculated for each image, including:
  • DICE
  • AJI
  • AJI+
  • PQ
  • Boundary F1 score
  • Recall
  • Precision
  • F1
  • FDR
  • True Positives
  • False Positives
  • False Negatives
• summary.csv
  A file summarizing detection statistics, including the average, standard deviation, and median of:
  • DICE
  • AJI
  • AJI+
  • PQ
  • Boundary F1 score (for all images)
• summary_detection.csv
  A file containing Recall, Precision, F1, and FDR calculated based on the accumulated True Positives, False Positives, and False Negatives for all images.

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2. vizualization_masks folder

This folder contains pixel-level visualizations of results for each image:

• Green pixels: True Positives (TP)
• Blue pixels: False Negatives (FN)
• Red pixels: False Positives (FP)

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3. plots folder

This folder contains violin plots generated based on data from the stats.csv file.

5. Try the Demo

• A folder named example_data contains demo masks for testing.
• Run the script with its default settings to generate example plots and results for reference.

6. Script to extract patches

• A script prepare_patches.py can be used to divide images into patches. You have to set paths to directories and run the script. Masks will be relabeled in a way that was mentioned before.

References

1. Implementation of Boundary F1 Score

   • bfscore_python GitHub Repository

2. Implementation of DICE, AJI, AJI+, and PQ is based on:

   • Graham, Simon, Vu, Quoc Dang, Raza, Shan E Ahmed, Azam, Ayesha, Tsang, Yee Wah, Kwak, Jin Tae, and Rajpoot, Nasir.
     Hover-net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images.
     Medical Image Analysis, Elsevier, 2019. Pages: 101563.
     DOI: 10.1016/j.media.2019.101563

Cite this repository

@misc{merta_segmentation_stats,
  author       = {Julia Merta},
  title        = {Segmentation Stats: Implementation of common segmentation and object detection metrics},
  howpublished = {\url{https://github.com/julimer228/segmentation_stats}},
  note         = {Accessed: 2025-07-22}
}