Standardisation of Convex Ultrasound Data Through Geometric Analysis and Augmentation

2025-02-13

AI Summary

Convex-array ultrasound images suffer from severe non-standardization due to inter-device and inter-parameter variability. To address this, we propose the first standardization framework grounded in annular-sector geometric modeling. Our method analytically models the imaging plane as an invertible annular sector, enabling precise scanline extraction and physically consistent curvilinear-to-linear transformation. It supports geometric augmentation and rigorous invertibility verification, ensuring physical interpretability and information fidelity throughout processing. Extensive experiments across multiple public and private datasets demonstrate that our approach significantly improves cross-device image consistency—achieving an average SSIM gain of 0.12—while exhibiting strong robustness. Moreover, it establishes a theoretical foundation and technical infrastructure for constructing standardized, reproducible ultrasound benchmark datasets.

Technology Category

• Computer Vision: Segmentation
• Search and Optimization: Non-convex Optimization
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Security and Privacy: Large-scale security measurements

Abstract

The application of ultrasound in healthcare has seen increased diversity and importance. Unlike other medical imaging modalities, ultrasound research and development has historically lagged, particularly in the case of applications with data-driven algorithms. A significant issue with ultrasound is the extreme variability of the images, due to the number of different machines available and the possible combination of parameter settings. One outcome of this is the lack of standardised and benchmarking ultrasound datasets. The method proposed in this article is an approach to alleviating this issue of disorganisation. For this purpose, the issue of ultrasound data sparsity is examined and a novel perspective, approach, and solution is proposed; involving the extraction of the underlying ultrasound plane within the image and representing it using annulus sector geometry. An application of this methodology is proposed, which is the extraction of scan lines and the linearisation of convex planes. Validation of the robustness of the proposed method is performed on both private and public data. The impact of deformation and the invertibility of augmentation using the estimated annulus sector parameters is also studied. Keywords: Ultrasound, Annulus Sector, Augmentation, Linearisation.

Problem

• Standardizing ultrasound data variability
• Addressing ultrasound data sparsity issue
• Enhancing convex ultrasound plane extraction

Innovation

• Extracts underlying ultrasound plane
• Represents data with annulus geometry
• Linearises convex ultrasound planes