USFetal: Tools for Fetal Brain Ultrasound Compounding

2026-01-11

AI Summary

Fetal brain ultrasound imaging is hindered by view-dependent artifacts, operator variability, and limited field of view, impeding accurate interpretation and quantitative analysis. To address these challenges, this work proposes a consistent volumetric reconstruction framework based on multi-view 3D ultrasound data fusion, systematically integrating multiscale fusion, transformation-based registration, variational optimization, and deep learning. Emphasizing scenarios with absent ground-truth labels, the study advances novel unsupervised and self-supervised strategies. Key contributions include the first systematic taxonomy of compound fetal brain ultrasound methods, the introduction of two new unsupervised/self-supervised deep learning architectures, and the release of the open-source USFetal toolbox. Comprehensive evaluation across ten fetal brain ultrasound datasets demonstrates superior performance in both quantitative image quality metrics and radiologist assessments, significantly advancing reproducible research in this domain.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multimodal Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization

Abstract

Ultrasound offers a safe, cost-effective, and widely accessible technology for fetal brain imaging, making it especially suitable for routine clinical use. However, it suffers from view-dependent artifacts, operator variability, and a limited field of view, which make interpretation and quantitative evaluation challenging. Ultrasound compounding aims to overcome these limitations by integrating complementary information from multiple 3D acquisitions into a single, coherent volumetric representation. This work provides four main contributions: (1) We present the first systematic categorization of computational strategies for fetal brain ultrasound compounding, including both classical techniques and modern learning-based frameworks. (2) We implement and compare representative methods across four key categories - multi-scale, transformation-based, variational, and deep learning approaches - emphasizing their core principles and practical advantages. (3) Motivated by the lack of full-view, artifact-free ground truth required for supervised learning, we focus on unsupervised and self-supervised strategies and introduce two new deep learning based approaches: a self-supervised compounding framework and an adaptation of unsupervised deep plug-and-play priors for compounding. (4) We conduct a comprehensive evaluation on ten multi-view fetal brain ultrasound datasets, using both expert radiologist scoring and standard quantitative image-quality metrics. We also release the USFetal Compounding Toolbox, publicly available to support benchmarking and future research. Keywords: Ultrasound compounding, fetal brain, deep learning, self-supervised, unsupervised.

Problem

• Ultrasound compounding
• fetal brain
• view-dependent artifacts
• operator variability
• limited field of view

Innovation

• ultrasound compounding
• fetal brain
• deep learning
• self-supervised
• unsupervised