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

Dimensions overall score 7.0

• A 3-stage progressive learning framework based on SwinUNETR was implemented for LA scar segmentation.method 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract explicitly describes the 3-stage framework and mentions SwinUNETR as the base model.

  Verificationpartial

  partial

• An anatomy-aware spatially weighted loss was introduced to constrain scar predictions to anatomically plausible LA wall regions.method 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract clearly states the introduction and purpose of this loss function.

  Verificationpartial

  partial

• The proposed method achieved a Dice score of 0.50 for LA scar segmentation on the LASCARQS dataset.result 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract provides specific quantitative results for LA scar segmentation.

  Verificationpartial

  partial

• The proposed method outperformed a one-stage scar segmentation approach in terms of Dice score (0.50 vs 0.49).result 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract directly compares the proposed method's performance against a baseline one-stage method.

  Verificationpartial

  partial

• The progressive learning strategy aims to address challenges in LA scar segmentation including low contrast, annotation variability, and lack of anatomical constraints.technical 90%
  Evidencepartial

  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.

  Implicationpartial

  The abstract outlines the challenges and states that the proposed strategy is inspired to address them.

  Verificationpartial

  partial

• The dual-task learning stage jointly optimizes LA cavity and scar segmentation.method 95%
  Evidencepartial

  In this second stage, we adopt a dual-task learning strategy to optimize LA cavity and scar segmentation jointly.

  Implicationpartial

  The description of Stage II in the abstract and figure clearly indicates joint optimization.

  Verificationpartial

  partial

• The LASCARQS public dataset was used for validation with 5-fold cross-validation.method 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract specifies the dataset and the validation methodology.

  Verificationpartial

  partial

• A 3-stage progressive learning framework based on SwinUNETR was implemented for LA scar segmentation.method 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract explicitly states the implementation of a 3-stage framework based on SwinUNETR.

  Verificationpartial

  partial

• The proposed approach incorporates an anatomy-aware spatially weighted loss function.method 95%
  Evidencepartial

  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.

  Implicationpartial

  The abstract clearly describes the introduction of this specific loss function.

  Verificationpartial

  partial

• The LA segmentation achieved a Dice score of 0.94 in preliminary results.result 95%
  Evidencepartial

  LA segmentation had Dice score of 0.94

  Implicationpartial

  This is a specific quantitative result directly stated in the abstract.

  Verificationpartial

  partial

• The LA scar segmentation achieved a Dice score of 0.50.result 95%
  Evidencepartial

  LA scar segmentation achieved Dice score of 0.50

  Implicationpartial

  This is a specific quantitative result directly stated in the abstract.

  Verificationpartial

  partial

• The LA scar segmentation achieved a Hausdorff Distance of 11.84 mm.result 95%
  Evidencepartial

  Hausdorff Distance of 11.84 mm

  Implicationpartial

  This is a specific quantitative result directly stated in the abstract.

  Verificationpartial

  partial

Competitive landscape

A progressive learning framework for more reliable left atrial scar segmentation in cardiac MRI, inspired by clinical workflows.