Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation
A study across six echocardiographic datasets finds that domain shift in left ventricular segmentation largely stems from field-of-view and framing inconsistencies, not acoustic differences. Geometry-aware preprocessing improves transfer, and representation-specific discrepancy measures can predict performance drops with high accuracy, supporting mask-free monitoring.
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[Submitted on 22 Jul 2026]
Title:Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation
View a PDF of the paper titled Domain Shift in Echocardiography: Interpretable Quantification and Prediction of Cross-Dataset Left Ventricular Segmentation, by Soroush Elyasi and 3 other authors
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Abstract:Cross-dataset generalisation remains a major barrier to clinical deployment of echocardiographic left ventricular segmentation, yet the sources of this shift are rarely disentangled. We examined whether transfer degradation could be estimated before deployment using handcrafted ultrasound descriptors, VAE latent features, and segmentation-derived latent features across six echocardiographic datasets. Geometry-aware preprocessing substantially improved several poor transfer cases, suggesting that much of the apparent domain shift reflects field-of-view and framing inconsistencies rather than intrinsic acoustic differences alone. Intensity z-normalisation changed dataset separability by less than 0.005, indicating that brightness and contrast are not the dominant shift axis. Absolute Dice drop on held-out source-target pairs was predicted with an R-squared value of 0.612, an MAE of 0.082, and a Spearman rho of 0.681. The variant without LV and fan-shaped features retained approximately 70% of this explanatory power, supporting mask-free transfer-risk monitoring. The most informative discrepancy measure depended on the representation, with CMD strongest in z-normalised handcrafted features, with an absolute r of approximately 0.86 and an R-squared value of approximately 0.70; log-Wasserstein strongest in VAE space, with an r of approximately -0.90 and an R-squared value of approximately 0.81; and log-MMD strongest in LV-segmentation latent features, with an r of approximately -0.92 and an R-squared value of approximately 0.84. Apparent vendor effects were largely dataset-confounded. Echocardiographic domain shift is therefore structured and measurable, and its impact on segmentation can be partly reduced through geometry-aware preprocessing and anticipated using representation-specific transfer-risk estimation.
Subjects:
Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2607.19643 [cs.CV]
(or arXiv:2607.19643v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.19643
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Soroush Elyasi [view email] [v1] Wed, 22 Jul 2026 00:48:57 UTC (2,832 KB)
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