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

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

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Canonical route: /signal-canvas/progressive-learning-with-anatomical-priors-for-reliable-left-atrial-scar-segmentation-from-late-gadolinium-enhancement

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Proof freshness: stale
Proof status: unverified
Display score: 7/10
Last proof check: 2026-03-30
Score updated: 2026-04-02
Score fresh until: 2026-05-02
References: 49
Source count: 3
Coverage: 50%

This page is showing the last landed evidence receipt and score bundle because the latest proof data is outside the freshness window.

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Canonical ID progressive-learning-with-anatomical-priors-for-reliable-left-atrial-scar-segmentation-from-late-gadolinium-enhancement | Route /signal-canvas/progressive-learning-with-anatomical-priors-for-reliable-left-atrial-scar-segmentation-from-late-gadolinium-enhancement

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Evidence Receipt

Route status: building

Claims: 12

References: 49

Proof: Verification pending

Freshness state: computing

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

PDF: https://arxiv.org/pdf/2603.26186v1

Source count: 3

Coverage: 50%

Last proof check: 2026-03-30T21:54:27.767Z

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

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

Verdict

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Claim map

Strong 12Mixed 0Weak 0

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
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
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
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
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
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
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
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
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
Evidencepartial
LA segmentation had Dice score of 0.94
Implicationpartial
This is a specific quantitative result directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
LA scar segmentation achieved Dice score of 0.50
Implicationpartial
This is a specific quantitative result directly stated in the abstract.
Verificationpartial
partial
Evidencepartial
Hausdorff Distance of 11.84 mm
Implicationpartial
This is a specific quantitative result directly stated in the abstract.
Verificationpartial
partial

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

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