segobe 0.1.1

Object-Based Identification Segmentation Evaluation for cells

SEGOBE - Object-Based Evaluation of Segmentation Results

Segobe is a minimal, lightweight package for segmentation mask evaluation against a reference (ground-truth) mask. It performs cell matching and computes metrics like the intersection-over-union (IoU), Dice, and classifies errors as splits, merges, and catastrophes, based on the descriptions in Greenwald et al. 2022 and Schwartz et al. 2024, with the added functionality of cost matrix selection, or matching approach. Designed for cell segmentation evaluation, it can handle large batches of samples efficiently.

Installation

Option 1. Install directory from the repository (recommended for development)

If you plan to develop or modify Segobe, install it in editable mode:

# Clone the repository
git clone https://github.com/schapirolabor/segobe.git
cd segobe

# (Optional) create the conda environment
conda env create -f environment.yml
conda activate segobe_env

# Install in editable/development mode
pip install -e .

The -e flag (editable mode) means the changes to the source code are immediately reflected without reinstalling.

Option 2. Install directly from GitHub

Once the repository is made public, users can install it directly via URL:

pip install git+https://github.com/schapirolabor/segobe.git

Verify installation with

python -m segobe --help

or simply:

segobe --help

to see the CLI help message.

CLI

segobe \
    --input samples.csv \
    --output_dir results \
    --basename testing \
    --iou_threshold 0.5 \
    --graph_iou_threshold 0.1 \
    --unmatched_cost 0.4 \
    --save_plots

Argument	Long form	Description
-i	--input_csv	File path to CSV with columns: sampleID, ref_mask, eval_mask, category
-o	--output_dir	Directory to save output metrics and plots
-b	--basename	Unique basename used when saving metrics and plots
	--iou_threshold	IoU threshold for cell matching (0-1, default: 0.5). Match is true if pair is selected with linear_sum_assignment and IoU above this threshold.
	--graph_iou_threshold	Graph IoU threshold for error detection (0-1, default: 0.1). Minimal IoU for cells to be considered 'connected'.
	--unmatched_cost	Cost for unmatched objects in the cost matrix (0-1, default: 0.4)
	--cost_matrix_metric	Specify which metric should be used for cost matrix construction (default: 'iou', other options 'dice', 'moc' - see details here) note that only IoU is currently supported
	--save_plots	Boolean specifying whether plots (barplot grouped by category and row-specific error overview) are saved
	--plot_target_size	Size in pixels of the plot error types subfigures. If the inputs are larger, they will be approximately downsampled by a scale factor. If that scale factor is larger than 4, boundaries will not be drawn. (default: 600)
	--version	Prints tool version.

Input format

Example of input CSV with potential usecase, comparing two methods across two samples (e.g. same ROI).

sampleID	ref_mask	eval_mask	category
sample1	path/to/groundtruth1.tif	path/to/prediction1_1.tif	method1
sample1	path/to/groundtruth1.tif	path/to/prediction1_2.tif	method2
sample2	path/to/groundtruth2.tif	path/to/prediction2_1.tif	method1
sample2	path/to/groundtruth2.tif	path/to/prediction2_2.tif	method2

Outputs

Expected outputs given the CLI example and input CSV:

results/
├── testing_metrics.csv                          # Full per-sample segmentation evaluation results
├── testing_summary.csv                          # Aggregated metrics summarized by category
├── plots/
│   ├── testing_metrics_barplot.png              # Optional barplot of summary metrics (if --save_plots)
│   ├── testing_sample1_method1_error_plot.png   # Optional per-sample error visualizations (if --save_plots)
│   ├── testing_sample2_method1_error_plot.svg
│   ├── testing_sample1_method2_error_plot.svg
│   └── testing_sample2_method2_error_plot.png

Captured metrics in the summary.csv across all inputs grouped per category:

• IoU: mean and std
• Dice: mean and std
• Precision: mean and std
• Recall: mean and std
• F1 score: mean and std
• Splits: counts
• Merges: counts
• Catastrophes: counts

Captured metrics in the metrics.csv for each input CSV row:

• IoU: mean and list of all values
• Dice: mean and list of all values
• Precision
• Recall
• F1 score
• Splits: counts and dictionary of matched predictions to GTs
• Merges: counts and dictionary of matched GTs to predictions
• Catastrophes: counts and dictionary of groups of GTs and predictions involved
• True postives: counts
• False positives: counts
• False negatives: counts
• FP_list: list of false positive labels
• FN_list: list of false negative labels
• TTP_gt: list of GT labels of true positives
• TTP_preds: list of prediction labels of true positives
• total_cells: count of GT labels
• total_pred_cells: count of prediction labels

Detailed description

Check the Detailed overview for a deeper clarification of tool functionality.

Contributing

Contributions, issues, and feature requests are welcome!
Feel free to open a pull request or submit an issue on GitHub Issues.

Before submitting a PR:

• Run tests (not yet applicable)
• Follow existing code style and documentation patterns

Citing

If you use Segobe in your work, please cite:

Bestak, K. Segobe: Object-Based Evaluation of Segmentation Results. Available at: https://github.com/schapirolabor/segobe

Note that for referencing the segmentation errors as used here, Greenwald et al. 2022 needs to be cited.