mikan-rs 🍊

A medical image kit for segmentation metrics evaluation, native Rust support, and Python bindings for cross-language performance.

🎨 Features

• 🚀 Blazingly Fast: Written in Rust with high parallelization; speeds are 10-100x faster than medpy (depends on the number of cores in your CPU), especially for Hausdorff distance calculations.

• 🎯 Simple: The API is so intuitive that you can start using it immediately while reading the documentation in just one minute!

• 🧮 Comprehensive Metrics: Easily to compute almost all of segmentation metrics:

  • Confusion Matrix Based:

    • Dice/IoU
    • TP/TN/FP/FN
    • Sensitivity/Specificity/Precision
    • Accuracy/Balanced Accuracy
    • ARI/FNR/FPR/F-score
    • Volume Similarity
    • MCC/nMCC/aMCC

  • Distance Based:

    • Hausdorff Distance (HD)
    • Hausdorff Distance 95 (HD95)
    • Average Symmetric Surface Distance (ASSD)
    • Mean Average Surface Distance (MASD)

🔨 Install

cargo add mikan-rs for rust project.

pip install mikan-rs for python.

🥒 Develop

maturin dev

📘 Usages

import mikan
import SimpleITK as sitk

gt = sitk.ReadImage("gt.nii.gz", sitk.sitkUInt8)
pred = sitk.ReadImage("pred.nii.gz", sitk.sitkUInt8)

e = mikan.Evaluator(gt, pred)
e.labels(1).metrics("dice")

For details, please refer to the python examples and rust examples.

🎄 Related Projects

• medpy: A well-known package for calculating segmentation metrics, with excellent documentation and implementation.
• miseval: A framework capable of calculating a large number of segmentation metrics.
• seg_metrics: A package for segmentation metrics that supports batch data calculation and CSV output, making it very convenient.
• MetricsReloaded: A new recommendation framework for biomedical image analysis validation, published in Nature Methods.

📃 Citation

If you use this software, we would appreciate it if you could include an mikan emoji 🍊 in your paper.

🍚 Q&A

Q: Why are my results different from seg_metrics/miseval/MetricsReloaded?

A: They are wrong. Of course, we might be wrong too. PRs to fix issues are welcome!

🔒 License

Licensed under either of the following licenses, at your choice:

Apache License, Version 2.0 (See LICENSE-APACHE or visit http://www.apache.org/licenses/LICENSE-2.0)

MIT License (See LICENSE-MIT or visit http://opensource.org/licenses/MIT)

Unless you explicitly state otherwise, any contribution intentionally submitted for inclusion in this project, as defined by the Apache License 2.0, will be dual-licensed under the above licenses without any additional terms or conditions.