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Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI

Jing Zhang, Bastien Bergere, Emilie Bollache, Jonas Leite, Mikaël Laredo, Alban Redheuil, Nadjia Kachenoura

Abstract

Cardiac MRI late gadolinium enhancement (LGE) enables non-invasive identification of left atrial (LA) scar, whose spatial distribution is strongly associated with atrial fibrillation (AF) severity and recurrence. However, automatic LA scar segmentation remains challenging due to low contrast, annotation variability, and the lack of anatomical constraints, often leading to non-reliable predictions. Accordingly, our aim was to propose a progressive learning strategy to segment LA scar from LGE images inspired from a clinical workflow. A 3-stage framework based on SwinUNETR was implemented, comprising: 1) a first LA cavity pre-learning model, 2) dual-task model which further learns spatial relationship between LA geometry and scar patterns, and 3) fine-tuning on precise segmentation of the scar. Furthermore, we introduced an anatomy-aware spatially weighted loss that incorporates prior clinical knowledge by constraining scar predictions to anatomically plausible LA wall regions while mitigating annotation bias. Our preliminary results obtained on validation LGE volumes from LASCARQS public dataset after 5-fold cross validation, LA segmentation had Dice score of 0.94, LA scar segmentation achieved Dice score of 0.50, Hausdorff Distance of 11.84 mm, Average Surface Distance of 1.80 mm, outperforming only a one-stage scar segmentation with 0.49, 13.02 mm, 1.96 mm, repectively. By explicitly embedding clinical anatomical priors and diagnostic reasoning into deep learning, the proposed approach improved the accuracy and reliability of LA scar segmentation from LGE, revealing the importance of clinically informed model design.

Progressive Learning with Anatomical Priors for Reliable Left Atrial Scar Segmentation from Late Gadolinium Enhancement MRI

Abstract

Cardiac MRI late gadolinium enhancement (LGE) enables non-invasive identification of left atrial (LA) scar, whose spatial distribution is strongly associated with atrial fibrillation (AF) severity and recurrence. However, automatic LA scar segmentation remains challenging due to low contrast, annotation variability, and the lack of anatomical constraints, often leading to non-reliable predictions. Accordingly, our aim was to propose a progressive learning strategy to segment LA scar from LGE images inspired from a clinical workflow. A 3-stage framework based on SwinUNETR was implemented, comprising: 1) a first LA cavity pre-learning model, 2) dual-task model which further learns spatial relationship between LA geometry and scar patterns, and 3) fine-tuning on precise segmentation of the scar. Furthermore, we introduced an anatomy-aware spatially weighted loss that incorporates prior clinical knowledge by constraining scar predictions to anatomically plausible LA wall regions while mitigating annotation bias. Our preliminary results obtained on validation LGE volumes from LASCARQS public dataset after 5-fold cross validation, LA segmentation had Dice score of 0.94, LA scar segmentation achieved Dice score of 0.50, Hausdorff Distance of 11.84 mm, Average Surface Distance of 1.80 mm, outperforming only a one-stage scar segmentation with 0.49, 13.02 mm, 1.96 mm, repectively. By explicitly embedding clinical anatomical priors and diagnostic reasoning into deep learning, the proposed approach improved the accuracy and reliability of LA scar segmentation from LGE, revealing the importance of clinically informed model design.

Paper Structure

This paper contains 13 sections, 9 equations, 3 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. LA scar segmentation
  4. Progressive learning
  5. Methodology
  6. Loss function and data augmentation
  7. Progressive learning framework
  8. Experiments
  9. Dataset and preprocessing
  10. Experimental protocol
  11. Results
  12. Ablation study
  13. Conclusion

Figures (3)

  • Figure 1: Progressive learning for LA scar segmentation from LGE MRI. In Stage I, the model learns to identify the easy LA structure; in Stage II, the model inherited from Stage I, and learns to identify LA and scar at the same time with the use of two decoders as well as LA wall constraint for scar; in Stage III, the model inherited from Stage II, and keeps fine-tuning scar segmentation.
  • Figure 2: Examples of cropped axial images from LGE MRI volumes along with LA cavity (green) as well as scar (red) expert annotations from LASCARQS public dataset.
  • Figure 3: Examples of LA cavity and scar segmentation from LASCARQS public dataset. From left to right columns: input LGE MRI images, LA ground truth (GT), LA predicitons from Stage I+II. LA scar GT, scar predictions from Stage I+II and Stage I+II+III respectively. The Dice scores are indicated in red.