Project-MONAI / Project-MONAI/MONAI
Implementation of the centre line Dice score / topology precision / topology sensitivity
Nobody has claimed this yet.
- Dominant language
- Python
- Stars
- 8.7k
- Forks
- 1.6k
- Avg merge
- 5d 1h
- Merged PRs (30d)
- 20
Description
Is your feature request related to a problem? Please describe.
Need for metrics to evaluate connectivity - particularly useful for evaluation of vascular segmentations
Describe the solution you'd like
Implementation of the three main topology metrics relying on skeletonisation of reference and binary predictions.
Notation for indication in the below formula:
$S_{Pred}$ - Skeleton of the prediction
$S_{Ref}$ - Skeleton of the reference
$Pred$ - Prediction
$Ref$ - Reference
Topology precision: $T_{prec}(S_{Pred},Ref) = \dfrac{\vert S_{Pred} \bigcap Ref \vert}{\vert S_{Pred}\vert}$
Topology sensitivity $T_{sens}(S_{Ref}, Pred) = \dfrac{\vert S_{Ref} \bigcap Pred\vert}{\vert S_{Ref} \vert}$
The centre line DSC uses the skeletonisation of a binary segmentation. It is expressed as a function of the topology precision and the topology sensitivity
$clDSC = \dfrac{2*T_{prec}(S_{Pred},Ref) * T_{sens}(S_{Ref},Pred)}{T_{prec}(S_{Pred},Ref) + T_{sens}(S_{Ref},Pred)}$
Contributor guide
First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
No file or test is named in the issue. Start from the three formulas and the stated skeletonisation of binary predictions and references; done means the centre-line Dice, topology precision, and topology sensitivity metrics are available for vascular-segmentation evaluation.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- machine-learning
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 42/100