dorsal/arxiv
View SchemaUSFetal: Tools for Fetal Brain Ultrasound Compounding
| Authors | Mohammad Khateri, Morteza Ghahremani, Sergio Valencia, Camilo Jaimes, Alejandra Sierra, Jussi Tohka, P. Ellen Grant, Davood Karimi |
|---|---|
| Categories | |
| ArXiv ID | 2601.06726vv1 |
| URL | https://arxiv.org/abs/2601.06726 |
| License | http://creativecommons.org/licenses/by/4.0/ |
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.
{
"annotation_id": "830f5e35-758a-4697-9a6a-eee65eecff92",
"date_created": "2026-02-17T05:53:08.099000Z",
"date_modified": "2026-02-17T05:53:08.099000Z",
"file_hash": "8cf79dadaed5347d70b245a7cbdbb82bac691e33cbfdc18159ff06b8a7fd3e76",
"private": false,
"record": {
"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.",
"arxiv_id": "2601.06726",
"authors": [
"Mohammad Khateri",
"Morteza Ghahremani",
"Sergio Valencia",
"Camilo Jaimes",
"Alejandra Sierra",
"Jussi Tohka",
"P. Ellen Grant",
"Davood Karimi"
],
"categories": [
"eess.IV",
"cs.CV"
],
"license": "http://creativecommons.org/licenses/by/4.0/",
"title": "USFetal: Tools for Fetal Brain Ultrasound Compounding",
"url": "https://arxiv.org/abs/2601.06726",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "21378c64-5769-4267-9e28-73e14dcca832",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
"version": "0.1.0"
},
"user_id": 1000002
}