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A Strong Linear Baseline for Whole-Heart Cardiac Sh... | AI Research

Key Takeaways

  • A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model addresses the challenge of uni...
  • Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence.
  • Among released cardiac shape resources, none we identified carries the atrial appendage, pulmonary veins, and caval stumps as separate blocks in one mesh.
  • Completion benchmarks also compare deep models against a least-squares projection onto shape modes, not the conditional estimator the same fitted model implies.
  • A five-refit mask-conditioned graph variational autoencoder reached 5.248 mm and nearest-neighbour retrieval 8.931 mm.
Paper AbstractExpand

Public cardiac cohorts annotate different subsets of the heart, so shapes from separate sources cannot be pooled without shared correspondence. Among released cardiac shape resources, none we identified carries the atrial appendage, pulmonary veins, and caval stumps as separate blocks in one mesh. Completion benchmarks also compare deep models against a least-squares projection onto shape modes, not the conditional estimator the same fitted model implies. We release an eleven- structure cardiac computed-tomography (CT) statistical shape model, built from 383 automatically labelled cases in 11 571-vertex correspondence, and compare completion estimators under one frozen internal split and endpoint. On a 76-case internal list held out from fitting, a closed-form conditional-Gaussian estimator reconstructed the missing non-chamber structures at 3.717 mm mean per-vertex error, averaged equally over one, three, five, and nine observed structures. A five-refit mask-conditioned graph variational autoencoder reached 5.248 mm and nearest-neighbour retrieval 8.931 mm. The paired difference was 1.531 mm (95% confidence interval 1.384 to 1.711), and the ordering held in a raw-coordinate sensitivity arm. Expert manual labels exist for 58 external CT cases, but our registered reference is close enough to score only five structures. There the closed-form estimator again had lower average surface distance, 95th-percentile Hausdorff distance, and Chamfer error for both completed atria. On a second public benchmark of 20 cases the reference was close enough for three of four completed structures, and the same ordering held there. Four structures have no expert reference. The released model and its completion operator support cohort-unification research on aligned CT, not clinical use.

A Strong Linear Baseline for Whole-Heart Cardiac Shape Completion on CT, with an Open Eleven-Structure Statistical Shape Model addresses the challenge of unifying cardiac datasets that contain different anatomical annotations. Because public cardiac cohorts often label different subsets of the heart, researchers cannot easily pool data without shared correspondence. This paper introduces an eleven-structure statistical shape model (SSM) and a completion operator that allows researchers to reconstruct missing cardiac structures from whatever subset of the heart is currently available in their data.

The Problem of Fragmented Cardiac Data

Public cardiac datasets are inconsistent in their anatomical coverage. Some provide only ventricular chambers, while others include various vessels or appendages. Existing models often rely on fixed input and output structures, meaning they cannot adapt to the specific, partial labels provided by different cohorts. Furthermore, previous benchmarks for cardiac shape completion have relied on least-squares projections, which the authors argue are less accurate than the conditional estimators available from a fitted statistical model.

A Unified Eleven-Structure Model

The authors released an SSM built from 383 computed tomography (CT) cases, establishing a shared correspondence across 11,571 vertices. This model covers eleven distinct structures: the left ventricle, myocardium, right ventricle, left atrium, right atrium, aorta, pulmonary artery, left atrial appendage, pulmonary veins, superior vena cava, and inferior vena cava. By using a single template warped into each patient’s space, the researchers created a resource that supports cohort-unification research, allowing for the reconstruction of missing structures based on the ones present in a given scan.

Performance of Completion Estimators

The study compared several methods for reconstructing missing structures, including a closed-form conditional-Gaussian estimator, a mask-conditioned graph variational autoencoder, and nearest-neighbor retrieval. On a held-out set of 76 cases, the closed-form conditional-Gaussian estimator achieved a mean per-vertex error of 3.717 mm. This outperformed the graph variational autoencoder (5.248 mm) and nearest-neighbor retrieval (8.931 mm). The authors note that the closed-form estimator consistently performed better across different test scenarios, including external datasets where expert manual labels were available for comparison.

Limitations and Intended Use

The authors emphasize that this work is intended to support cohort-unification research on aligned CT data and is not for clinical use. Several limitations are noted:

  • Cardiac Phase: The dataset includes non-gated acquisitions, meaning the cardiac phase is sampled arbitrarily and treated as unstructured variation.

  • Data Source: The internal model was built using automatically generated labels from TotalSegmentator v2, which the authors categorize as "silver-standard." * Validation: While the model was tested against external expert-labeled cohorts, the reference-closeness rule meant that only a subset of the eleven structures could be scored against expert manual labels.

  • Scope: The model does not represent total myocardial mass, as it only includes the left-ventricular wall and blood-pool surfaces for the other ten structures.

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