This project is used for the evalation for EM 2D and 3D image segmentation task.

You can use one of the following two commands to install the required packages:

conda install --yes --file requirements.txt
pip install -r requirements.txt

For each of the challenge categories, you find an evaluation class in cremi.evaluation, which are NeuronIds, Clefts, and SynapticPartners.

After you read a test file test and a ground truth file truth, you can evaluate your results by instantiating these classes as follows:

from cremi.evaluation import NeuronIds, Clefts, SynapticPartners

neuron_ids_evaluation = NeuronIds(truth.read_neuron_ids())
(voi_split, voi_merge) = neuron_ids_evaluation.voi(test.read_neuron_ids())
adapted_rand = neuron_ids_evaluation.adapted_rand(test.read_neuron_ids())

clefts_evaluation = Clefts(test.read_clefts(), truth.read_clefts())
fp_count = clefts_evaluation.count_false_positives()
fn_count = clefts_evaluation.count_false_negatives()
fp_stats = clefts_evaluation.acc_false_positives()
fn_stats = clefts_evaluation.acc_false_negatives()

synaptic_partners_evaluation = SynapticPartners()
fscore = synaptic_partners_evaluation.fscore(
    test.read_annotations(),
    truth.read_annotations(),
    truth.read_neuron_ids())

We provide Dice coefficient, Jaccard coefficient, Precision, Precision, F1 score.

Run the following command to use the tool:

python eval_binary_seg.py --gt 'path to gt mask' --pre 'path to pre mask' --save_file 'results/evaluation_results.txt'

The following steps will be executed by the script:

1. Load the following 3D arrays:

• GT segmentation volume
• prediction segmentation volume

2. Calculate the Dice coefficient, Jaccard coefficient, Precision, Precision, F1 score

3. Save the results to txt file

Run the following command to use the tool:

python eval_instance_seg.py -gt demo_data/lucchi_gt_test.h5 -p demo_data/lucchi_pred_UNet_label_test.h5 -ph demo_data/lucchi_pred_UNet_heatmap_test.h5

The following steps will be executed by the script:

1. Load the following 3D arrays:

• GT segmentation volume
• prediction segmentation volume
• model prediction matrix / scores matrix in order to get the prediciton score of each voxel

2. Create the necessary tables to compute the mAP:

• iou_p.txt contains the different prediction ids, the prediction scores, and their matched ground trught (gt) ids. Each prediciton is matched with gt ids from 4 different size ranges (based on number of instance voxels). Each of these ranges contains the matched gt id, its size and the intersection over union (iou) score.
• iou_fn.txt contains false negatives, as well as instances that have been matched with a worse iou than another instance.

3. Evaluate the model performance with mAP by using the 3D optimized evaluation script and the 2 tables mentioned above.

We provide PSNR, SSIM, LPIPS, Foutier Ring Correlation scores.

Run the following command to use the tool:

python eval_binary_seg.py --gt 'path to gt mask' --pre 'path to pre mask' --save_file 'results/evaluation_results.txt'

The following steps will be executed by the script:

1. Load the following 3D arrays:

• GT segmentation volume
• prediction segmentation volume

2. Calculate the scores

3. Save the results to txt file

Evaluation code contributed by:

• Jan Funke
• Juan Nunez-Iglesias
• Philipp Hanslovsky
• Stephan Saalfeld