Segmentation Metrics

Volumetric binary mask segmentation accuracy metrics

Scope

A small package for assessing the accuracy of binary segmentations. There are lots of metrics that can be used to compare how close two segmentations are, here voxel overlap, surface and volume based metrics are all calculated at once and returned either as individual metrics, a dictionary or a Pandas DataFrame.

The surface based metrics in this package are calculated using code from deepmind's surface-distance repository, however as this is not available as a PyPI package, the code has been included as a submodule here.

Installation

Segmentation Metrics is available on PyPI and can be installed using pip: bash pip install segmentationmetrics or if you're a conda user: bash conda install segmentationmetrics -c conda-forge Alternatively, you can install from source: bash git clone https://github.com/alexdaniel654/Segmentation_Metrics.git cd Segmentation_Metrics pip install -e .

Calculated Metrics

Voxel overlap based metrics

• Dice Score/F1 Score
• Jaccard Score
• Sensitivity
• Specificity
• Precision
• Accuracy

Surface based metrics

• Mean Surface Distance (in mm) - The symmetric mean surface distance is returned by default i.e. the mean of the distance from surface A to surface B and surface B to surface A.
• Hausdorff Distance (in mm) - Computes the robust distance based on the percentile of distances rather than the maximum distance.

Volume based metrics

• Volume Difference (in millilitres)

Example Usage

```python import nibabel as nib # Package for reading MRI data import segmentationmetrics as sm

imgmanual = nib.load('maskmanuallysegmented.nii.gz') # Load manually generated ground truth mask imgautomatic = nib.load('maskautomaticallysegmented.nii.gz') # Load automatically generated mask

Get voxel data from image object

maskmanual = imgmanual.getfdata() maskautomatic = imgautomatic.getfdata()

Get zoom from header

zoom = imgmanual.header.getzooms()

Generate metrics

metrics = sm.SegmentationMetrics(maskautomatic, maskmanual, zoom)

Print the dice score

print(f'The Dice score is {metrics.dice:.2f}') ```

The Dice score is 0.85

```python

Get and print a DataFrame containing all the scores for this mask pair

df = metrics.get_df() print(df) ```

Metric Score dice Dice 0.844512 jaccard Jaccard 0.730870 sensitivity Sensitivity 0.732352 specificity Specificity 0.999926 precision Precision 0.997239 accuracy Accuracy 0.990492 mean_surface_distance Mean Surface Distance 1.459697 hausdorff_distance Hausdorff Distance 7.027224 volume_difference Volume Difference -107.212906 true_volume True Volume 403.632624 predicted_volume Predicted Volume 296.419718

```python

As above but with asymmetric mean surface distance and Hausdorff distance defined by the 99th percentil rather than the 95th percentile.

metrics = sm.SegmentationMetrics(maskautomatic, maskmanual, zoom, symmetric=False, percentile=99) df = metrics.get_df() print(df) ```

Metric Score dice Dice 0.844512 jaccard Jaccard 0.73087 sensitivity Sensitivity 0.732352 specificity Specificity 0.999926 precision Precision 0.997239 accuracy Accuracy 0.990492 mean_surface_distance Mean Surface Distance (1.6603182056644057, 1.259075931110695) hausdorff_distance Hausdorff Distance 9.335755 volume_difference Volume Difference -107.212906 true_volume True Volume 403.632624 predicted_volume Predicted Volume 296.419718