Project-MONAI / Project-MONAI/MONAI

Implementation of the centre line Dice score / topology precision / topology sensitivity

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Feature request Module: metrics WG: Evaluation
Dominant language
Python
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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

$clDSC = \dfrac{2*T_{prec}(S_{Pred},Ref) * T_{sens}(S_{Ref},Pred)}{T_{prec}(S_{Pred},Ref) + T_{sens}(S_{Ref},Pred)}$

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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

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