Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection

Q: What is the clinical evidence from this study?

Population: Left ventricular non-compaction cardiomyopathy (LVNC) and hypertrophic cardiomyopathy (HCM) (n=379). Intervention: Semi-automatic myocardial segmentation refinement framework (blob-selection and manual correction methods with 5-fold cross-validation U-Net) vs. Baseline U-Net model and original QLVTHC segmentation targets. Primary outcome: Myocardial segmentation accuracy measured by the Dice coefficient for the Trabecular Zone (Dice coefficient increase up to 0.06).

Q: What does this research mean for the field?

A semi-automatic blob-selection segmentation refinement framework improves the U-Net Dice coefficient for the myocardial trabecular zone by up to 0.06 in cardiac MRI datasets. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Jaime Rafael Barón; Gregorio Bernabé; Pilar González‐Férez; José M. Garcı́a; Guillem Casas; Josefa González‐Carrillo

doi:10.3390/jcm14010271

January 6, 2025Journal of Clinical MedicineOpen Access

Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection

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Key result

Semi-automatic blob-selection refinement boosts U-Net trabecular segmentation accuracy by up to ~0.06 Dice points in LVNC.

Dice coefficient increase up to 0.06
n=379

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Why the study?

Accurate cardiac MRI left ventricular myocardial segmentation is essential to develop reliable deep learning models for diagnosing left ventricular non-compaction cardiomyopathy.

Does a semi-automatic segmentation refinement framework combining neural network cross-validation, blob-selection, and manual correction improve the Dice similarity coefficient of left ventricular myocardial segmentations on cardiac MRI in patients with hypertrophic cardiomyopathy and left ventricular non-compaction?

Key Points

Develop and evaluate a semi-automatic framework to refine myocardial segmentations in cardiac MRI datasets, improving the data quality used to train deep learning models for diagnosing left ventricular non-compaction cardiomyopathy.
Implemented a semi-automatic refinement framework combining neural network outputs with expert manual corrections, an automated blob-selection method to correct segmentation hallucinations, and cross-validation using U-Net models.
Trained and evaluated five U-Net models on modified cardiac MRI datasets across three hospital cohorts (populations P, X, and H) using training sets of 432 images.
Baseline U-Net models achieved Trabecular Zone Dice coefficients between 0.82 and 0.87, whereas the blob-selection and manual correction methods increased Dice coefficients by up to 0.06 in population H and 0.02 in population P.
The blob-selection method achieved average Dice coefficients around 0.94 relative to original targets and required 20 to 30 s per image, compared with 2 to 3 min per image for manual correction.

Structured PICO

Does a semi-automatic segmentation refinement framework combining neural network cross-validation, blob-selection, and manual correction improve the Dice similarity coefficient of left ventricular myocardial segmentations on cardiac MRI in patients with hypertrophic cardiomyopathy and left ventricular non-compaction?

P

Population

379 patients (3,044 total slices; 545 representative slices analyzed) across three Spanish hospitals with hypertrophic cardiomyopathy (n=293), left ventricular non-compaction cardiomyopathy (n=28), or various heart conditions (n=58) undergoing non-contrast 1.5 T cardiac MRI.

I

Intervention

Semi-automatic cardiac MRI segmentation refinement framework utilizing three approaches: (1) combining neural network outputs with expert-driven manual corrections via an interactive Image Fixer program, (2) a blob-selection method to correct segmentation errors and hallucinations, and (3) a 5-fold cross-validation process using a baseline U-Net model

C

Comparator

Baseline U-Net model from prior work and original segmentation targets from the semi-automated QLVTHC software tool

O

Outcome

Segmentation accuracy measured by the Dice similarity coefficient across myocardial regions (specifically the Trabecular Zone, External Layer, and Internal Cavity)surrogate

Main Result

Effect estimate: Dice coefficient increase up to 0.06

A semi-automatic cardiac MRI segmentation refinement framework utilizing blob-selection and U-Net cross-validation significantly improves trabecular zone segmentation accuracy and reduces manual processing time, providing higher-quality training data for automated left ventricular non-compaction diagnostic models.

Limitations

No improvement in Dice coefficient was observed for the X population Trabecular Zone, potentially due to lower representation of X images in the training sets.
The blob-selection method only allows changes proposed by the cross-validation method, which tends to homogenize results and may exclude changes that would increase accuracy.
The slight increase of approximately 0.03 in alignment for the blob-selection method may not be substantial enough to fully correct images on its own without initial manual corrections.
The blob-selection method only allows changes proposed by the cross-validation method, which tends to homogenize results and may exclude changes that would increase accuracy by capturing variations.
The slight alignment improvement of approximately 0.03 in average Dice coefficient with the blob-selection method may not be substantial enough to fully correct images on its own without initial manual corrections.

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Implication

May refine LVNC segmentation for model training; leaves open prospective validation before clinical adoption.

Abstract

Background: Accurate segmentation of the left ventricular myocardium in cardiac MRI is essential for developing reliable deep learning models to diagnose left ventricular non-compaction cardiomyopathy (LVNC). This work focuses on improving the segmentation database used to train these models, enhancing the quality of myocardial segmentation for more precise model training. Methods: We present a semi-automatic framework that refines segmentations through three fundamental approaches: (1) combining neural network outputs with expert-driven corrections, (2) implementing a blob-selection method to correct segmentation errors and neural network hallucinations, and (3) employing a cross-validation process using the baseline U-Net model. Results: Applied to datasets from three hospitals, these methods demonstrate improved segmentation accuracy, with the blob-selection technique boosting the Dice coefficient for the Trabecular Zone by up to 0.06 in certain populations. Conclusions: Our approach enhances the dataset’s quality, providing a more robust foundation for future LVNC diagnostic models.

Discussion

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Cite This Study

Barón et al. (2025) studied Left ventricular non-compaction cardiomyopathy (LVNC) and hypertrophic cardiomyopathy (HCM) (n=379). Semi-automatic myocardial segmentation refinement framework (blob-selection and manual correction methods with 5-fold cross-validation U-Net) vs. Baseline U-Net model and original QLVTHC segmentation targets was evaluated on Myocardial segmentation accuracy measured by the Dice coefficient for the Trabecular Zone (Dice coefficient increase up to 0.06). The semi-automatic blob-selection segmentation refinement method improved the U-Net Dice coefficient for the Trabecular Zone by up to 0.06 in the LVNC population compared to the baseline U-Net model.

synapsesocial.com/papers/6a75ecaf26e6939f8e7861ad https://doi.org/10.3390/jcm14010271

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Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection

Figures

Key Points

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

Semi-Automatic Refinement of Myocardial Segmentations for Better LVNC Detection

Figures

Key Points

Structured PICO

Main Result

Limitations

Ask about this paper

Implication

Abstract

Discussion

Cite This Study

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