surface-distance computes performance metrics based on surface distances between image segmentations. It focuses on quantifying the alignment of surfaces, providing insights beyond traditional pixel-based metrics. The library uses surface elements with area to represent surfaces for improved accuracy and consistency in distance calculations. This facilitates a deeper understanding of segmentation quality by evaluating surface-level discrepancies.
This library offers a comprehensive set of surface distance metrics, including Hausdorff distance and surface overlap, which are often challenging to implement directly. It provides a structured and efficient way to calculate these metrics, simplifying the evaluation process for segmentation tasks. The focus on surface elements allows for robust comparison even with variations in segmentation scale and detail.
- Average Surface Distance: Calculates the average distance between surface points of two segmentations.
- Hausdorff Distance: Computes the maximum distance between any point on one surface to the closest point on the other.
- Surface Overlap: Measures the degree of overlap between the surfaces of two segmentations.
- Surface Dice: Calculates the Dice coefficient based on surface element overlap.
- Volumetric Dice: Computes the Dice coefficient considering the volume of the segmented regions.
- Python Implementation: Provides a user-friendly Python API for calculating surface distances.
- Surface Element Representation: Uses surface elements with area to handle variable segmentation scales.
The surface-distance library is a well-established project with consistent maintenance and a history of updates. It has a reasonable number of stars, forks, and recent commits, indicating ongoing development and community interest. The documentation includes installation instructions and usage examples, making it relatively easy to integrate into existing workflows.
surface-distance is valuable for researchers and practitioners working on image segmentation, particularly those needing to evaluate the alignment of segmented regions. It allows for a more nuanced assessment of segmentation quality by focusing on surface-level characteristics. By providing readily available metrics and a clear API, the library simplifies the process of quantifying segmentation accuracy and facilitates comparisons between different segmentation algorithms.