USFetal: Tools for Fetal Brain Ultrasound Compounding

• URL: http://arxiv.org/abs/2601.06726v1
• Date: Sun, 11 Jan 2026 00:16:31 GMT
• Title: USFetal: Tools for Fetal Brain Ultrasound Compounding
• Authors: Mohammad Khateri, Morteza Ghahremani, Sergio Valencia, Camilo Jaimes, Alejandra Sierra, Jussi Tohka, P. Ellen Grant, Davood Karimi,
• Abstract summary: We present the first systematic categorization of computational strategies for fetal brain ultrasound compounding.<n>We introduce two new deep learning based approaches: a self-supervised compounding framework and an adaptation of unsupervised deep plug-and-play priors for compounding.
• Score: 36.580909261486646
• 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.

Related papers

• PRIMA: Pre-training with Risk-integrated Image-Metadata Alignment for Medical Diagnosis via LLM [9.229938448766484]
  PRIMA is a framework that integrates domain-specific knowledge into multi-modal representation learning.<n>Our framework achieves superior robustness without the need for massive data collection or exhaustive computational resources.
  arXiv  Detail & Related papers  (2026-02-26T18:07:52Z)
• Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos [58.71502465551297]
  Intrapartum Ultrasound Grand Challenge (IUGC) co-hosted with MICCAI 2024 was launched.<n>IUGC introduces a clinically oriented multi-task automatic measurement framework that integrates standard plane classification, fetal head-pubic symphysis segmentation, and biometry.<n>The challenge releases the largest multi-center intrapartum ultrasound video dataset to date, comprising 774 videos (68,106 frames) collected from three hospitals.
  arXiv  Detail & Related papers  (2026-02-13T13:28:22Z)
• FETAL-GAUGE: A Benchmark for Assessing Vision-Language Models in Fetal Ultrasound [2.8097961263689406]
  The demand for prenatal ultrasound imaging has intensified a global shortage of trained sonographers.<n>Deep learning has the potential to enhance sonographers' efficiency and support the training of new practitioners.<n>We present Fetal-Gauge, the first and largest visual question answering benchmark specifically designed to evaluate Vision-Language Models (VLMs)<n>Our benchmark comprises over 42,000 images and 93,000 question-answer pairs, spanning anatomical plane identification, visual grounding of anatomical structures, fetal orientation assessment, clinical view conformity, and clinical diagnosis.
  arXiv  Detail & Related papers  (2025-12-25T04:54:37Z)
• A Fully Open and Generalizable Foundation Model for Ultrasound Clinical Applications [77.3888788549565]
  We present EchoCare, a novel ultrasound foundation model for generalist clinical use.<n>We developed EchoCare via self-supervised learning on our curated, publicly available, large-scale dataset EchoCareData.<n>With minimal training, EchoCare outperforms state-of-the-art comparison models across 10 representative ultrasound benchmarks.
  arXiv  Detail & Related papers  (2025-09-15T10:05:31Z)
• HDC: Hierarchical Distillation for Multi-level Noisy Consistency in Semi-Supervised Fetal Ultrasound Segmentation [2.964206587462833]
  A novel semi-supervised segmentation framework, called HDC, is proposed incorporating adaptive consistency learning with a single-teacher architecture.<n>The framework introduces a hierarchical distillation mechanism with two objectives: Correlation Guidance Loss for aligning feature representations and Mutual Information Loss for stabilizing noisy student learning.
  arXiv  Detail & Related papers  (2025-04-14T04:52:24Z)
• Deep Spectral Methods for Unsupervised Ultrasound Image Interpretation [53.37499744840018]
  This paper proposes a novel unsupervised deep learning strategy tailored to ultrasound to obtain easily interpretable tissue separations. We integrate key concepts from unsupervised deep spectral methods, which combine spectral graph theory with deep learning methods. We utilize self-supervised transformer features for spectral clustering to generate meaningful segments based on ultrasound-specific metrics and shape and positional priors, ensuring semantic consistency across the dataset.
  arXiv  Detail & Related papers  (2024-08-04T14:30:14Z)
• LVM-Med: Learning Large-Scale Self-Supervised Vision Models for Medical Imaging via Second-order Graph Matching [59.01894976615714]
  We introduce LVM-Med, the first family of deep networks trained on large-scale medical datasets. We have collected approximately 1.3 million medical images from 55 publicly available datasets. LVM-Med empirically outperforms a number of state-of-the-art supervised, self-supervised, and foundation models.
  arXiv  Detail & Related papers  (2023-06-20T22:21:34Z)
• Align, Reason and Learn: Enhancing Medical Vision-and-Language Pre-training with Knowledge [68.90835997085557]
  We propose a systematic and effective approach to enhance structured medical knowledge from three perspectives. First, we align the representations of the vision encoder and the language encoder through knowledge. Second, we inject knowledge into the multi-modal fusion model to enable the model to perform reasoning using knowledge as the supplementation of the input image and text. Third, we guide the model to put emphasis on the most critical information in images and texts by designing knowledge-induced pretext tasks.
  arXiv  Detail & Related papers  (2022-09-15T08:00:01Z)
• Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning [19.267693026491482]
  Learn2Reg covers a wide range of anatomies: brain, abdomen and thorax, modalities: ultrasound, CT, MRI, populations: intra- and inter-patient and levels of supervision. Our complementary set of metrics, including robustness, accuracy, plausibility and speed enables unique insight into the current-state-of-the-art of medical image registration.
  arXiv  Detail & Related papers  (2021-12-08T09:46:39Z)
• Learning Binary Semantic Embedding for Histology Image Classification and Retrieval [56.34863511025423]
  We propose a novel method for Learning Binary Semantic Embedding (LBSE) Based on the efficient and effective embedding, classification and retrieval are performed to provide interpretable computer-assisted diagnosis for histology images. Experiments conducted on three benchmark datasets validate the superiority of LBSE under various scenarios.
  arXiv  Detail & Related papers  (2020-10-07T08:36:44Z)
• Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound [1.9814912982226993]
  We propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. The proposed approach is based on a GoogleNet architecture, fine-tuned for our data in two training steps. It achieves an AUC of 0.94, outperforming state-of-the-art models trained over a single modality.
  arXiv  Detail & Related papers  (2020-09-23T09:08:24Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.