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Decoupled External Forces in a Predictor-Corrector
Segmentation Scheme for LV Contours in Tagged MR Images
Jaume Garcia-Barnes, Albert Andaluz , Francesc Carreras and Debora Gil
{jaumegb, aandaluz,debora}@cvc.uab.cat
Introduction
• Most heart diseases lead to abnormal wall motion patterns
• Functional indicators need segmentation of myocardial region
• Few methods for automatic segmentation in TMR.
• Standard segmentation techniques fail in TMR:
Our contribution
Segmentation of the LV contours in TMR sequences at ED:
1. Preprocessing: semantic description of myocardial tissue
• texture: removing the tag pattern
• motion: discriminating static surrounding tagged tissue
2. Segmentation: a predictor-corrector scheme
(a) Predictor:
• based in a classical snake formulation
• driven by multiple external forces
(b) Corrector:
• PCA-based shape model
• codifies expected shapes
Methods
Predictor-corrector Semantic descriptor Multiple external forces
Results
Validation protocol Contours
• Shape models: Base, Mid, Apex
• Training set: 47 MR images (tag-free)
• Test set: 21 TMR images (grid present)
• 1st quality score: segmentation error (distance map)
• 2nd quality score : global &regional rotaton (AHA)
• Ground truth: manual (clinical expert)
Statistical analysis (µ ± σ)
px/mm2
Global ◦
A ◦
AL/L ◦
I ◦
IS /S◦
IL ◦
AS ◦
Base 1.60 ± 0.70 0.15 ± 0.11 0.45 ± 0.3 0.40 ± 0.20 0.40 ± 0.21 0.46 ± 0.25 0.32 ± 0.16 0.34 ± 0.25
Mid 1.90 ± 0.90 0.16 ± 0.15 0.39 ± 0.25 0.44 ± 0.30 0.59 ± 0.62 0.35 ± 0.19 0.35 ± 0.21 0.38 ± 0.26
Apex 1.74 ± 0.92 0.53 ± 0.49 0.70 ± 0.52 1.27 ± 1.40 0.60 ± 0.48 0.59 ± 0.44 - -
Segmentation Rotation
• error < 2 pixels (Pixel spacing:1.56 px/mm2
). • Global rotation is very stable against segmentation errors.
• Average error below 0.5 ◦
.
Conclusions
1. Semantic description ⇒ restricted to the LV region.
2. Multiple External forces ⇒initialization free, avoiding local minima.
3. Global rotation provides stable evaluation of heart pathologies in LV.
Future work
1. Inter observer variability.
2. Pathological cases.
3. Additional clinical scores.

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EMBC conference poster

  • 1. Decoupled External Forces in a Predictor-Corrector Segmentation Scheme for LV Contours in Tagged MR Images Jaume Garcia-Barnes, Albert Andaluz , Francesc Carreras and Debora Gil {jaumegb, aandaluz,debora}@cvc.uab.cat Introduction • Most heart diseases lead to abnormal wall motion patterns • Functional indicators need segmentation of myocardial region • Few methods for automatic segmentation in TMR. • Standard segmentation techniques fail in TMR: Our contribution Segmentation of the LV contours in TMR sequences at ED: 1. Preprocessing: semantic description of myocardial tissue • texture: removing the tag pattern • motion: discriminating static surrounding tagged tissue 2. Segmentation: a predictor-corrector scheme (a) Predictor: • based in a classical snake formulation • driven by multiple external forces (b) Corrector: • PCA-based shape model • codifies expected shapes Methods Predictor-corrector Semantic descriptor Multiple external forces Results Validation protocol Contours • Shape models: Base, Mid, Apex • Training set: 47 MR images (tag-free) • Test set: 21 TMR images (grid present) • 1st quality score: segmentation error (distance map) • 2nd quality score : global &regional rotaton (AHA) • Ground truth: manual (clinical expert) Statistical analysis (µ ± σ) px/mm2 Global ◦ A ◦ AL/L ◦ I ◦ IS /S◦ IL ◦ AS ◦ Base 1.60 ± 0.70 0.15 ± 0.11 0.45 ± 0.3 0.40 ± 0.20 0.40 ± 0.21 0.46 ± 0.25 0.32 ± 0.16 0.34 ± 0.25 Mid 1.90 ± 0.90 0.16 ± 0.15 0.39 ± 0.25 0.44 ± 0.30 0.59 ± 0.62 0.35 ± 0.19 0.35 ± 0.21 0.38 ± 0.26 Apex 1.74 ± 0.92 0.53 ± 0.49 0.70 ± 0.52 1.27 ± 1.40 0.60 ± 0.48 0.59 ± 0.44 - - Segmentation Rotation • error < 2 pixels (Pixel spacing:1.56 px/mm2 ). • Global rotation is very stable against segmentation errors. • Average error below 0.5 ◦ . Conclusions 1. Semantic description ⇒ restricted to the LV region. 2. Multiple External forces ⇒initialization free, avoiding local minima. 3. Global rotation provides stable evaluation of heart pathologies in LV. Future work 1. Inter observer variability. 2. Pathological cases. 3. Additional clinical scores.