Pregnancy

Mono2D: A Trainable Monogenic Layer for Robust Knee Cartilage Segmentation on Out-of-Distribution 2D Ultrasound Data

Automated knee cartilage segmentation using point-of-care ultrasound devices and deep-learning networks has the potential to enhance the management of knee osteoarthritis. However, segmentation algorithms often struggle with domain shifts caused by variations in ultrasound devices and acquisition parameters, limiting their generalizability. In this paper, we propose Mono2D, a monogenic layer tha...

Self-supervised Normality Learning and Divergence Vector-guided Model Merging for Zero-shot Congenital Heart Disease Detection in Fetal Ultrasound Videos

Congenital Heart Disease (CHD) is one of the leading causes of fetal mortality, yet the scarcity of labeled CHD data and strict privacy regulations surrounding fetal ultrasound (US) imaging present significant challenges for the development of deep learning-based models for CHD detection. Centralised collection of large real-world datasets for rare conditions, such as CHD, from large populations...

The 4D Human Embryonic Brain Atlas: spatiotemporal atlas generation for rapid anatomical changes using first-trimester ultrasound from the Rotterdam Periconceptional Cohort

Early brain development is crucial for lifelong neurodevelopmental health. However, current clinical practice offers limited knowledge of normal emb...

Robotic Ultrasound-Guided Femoral Artery Reconstruction of Anatomically-Representative Phantoms

Femoral artery access is essential for numerous clinical procedures, including diagnostic angiography, therapeutic catheterization, and emergency in...

ImplicitCell: Resolution Cell Modeling of Joint Implicit Volume Reconstruction and Pose Refinement in Freehand 3D Ultrasound

Freehand 3D ultrasound enables volumetric imaging by tracking a conventional ultrasound probe during freehand scanning, offering enriched spatial in...

SAS: Segment Anything Small for Ultrasound -- A Non-Generative Data Augmentation Technique for Robust Deep Learning in Ultrasound Imaging

Accurate segmentation of anatomical structures in ultrasound (US) images, particularly small ones, is challenging due to noise and variability in im...

CACTUS: An Open Dataset and Framework for Automated Cardiac Assessment and Classification of Ultrasound Images Using Deep Transfer Learning

Cardiac ultrasound (US) scanning is a commonly used techniques in cardiology to diagnose the health of the heart and its proper functioning. Therefo...

L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation

Accurate analysis of prenatal ultrasound (US) is essential for early detection of developmental anomalies. However, operator dependency and technica...

Data-Efficient Learning from Human Interventions for Mobile Robots

Mobile robots are essential in applications such as autonomous delivery and hospitality services. Applying learning-based methods to address mobile ...

Maestro: A 302 GFLOPS/W and 19.8GFLOPS RISC-V Vector-Tensor Architecture for Wearable Ultrasound Edge Computing

Most Wearable Ultrasound (WUS) devices lack the computational power to process signals at the edge, instead relying on remote offload, which introdu...

Machine learning for automated identification of anatomical landmarks in ultrasound periodontal imaging.

OBJECTIVES: To identify landmarks in ultrasound periodontal images and automate the image-based measurements of gingival recession (iGR), gingival hei...

Artificial intelligence-driven analysis of embryo morphokinetics in singleton, twin, and triplet pregnancies.

In recent years, the transfer of more than one embryo has become less frequent to diminish multiple pregnancies. Even so, there is still a risk of one...

Artificial Intelligence-Guided Lung Ultrasound by Nonexperts.

IMPORTANCE: Lung ultrasound (LUS) aids in the diagnosis of patients with dyspnea, including those with cardiogenic pulmonary edema, but requires techn...

Deep Learning-based Brain Age Prediction Using MRI to Identify Fetuses with Cerebral Ventriculomegaly.

Fetal ventriculomegaly (VM) and its severity and associated central nervous system (CNS) abnormalities are important indicators of high risk for impai...

Text phrase-mining in identifying and classifying maternal proteins and genes across preeclampsia and similar pathologies.

This study aims to demonstrate that text phrase-mining and natural language processing (NLP) can annotate huge quantities of obstetrics textual data f...

Generative augmentations for improved cardiac ultrasound segmentation using diffusion models

One of the main challenges in current research on segmentation in cardiac ultrasound is the lack of large and varied labeled datasets and the differ...

Weakly Supervised Segmentation Framework for Thyroid Nodule Based on High-confidence Labels and High-rationality Losses

Weakly supervised segmentation methods can delineate thyroid nodules in ultrasound images efficiently using training data with coarse labels, but su...

eXplainMR: Generating Real-time Textual and Visual eXplanations to Facilitate UltraSonography Learning in MR

eXplainMR is a Mixed Reality tutoring system designed for basic cardiac surface ultrasound training. Trainees wear a head-mounted display (HMD) and ...

[Research on intelligent fetal heart monitoring model based on deep active learning].

Cardiotocography (CTG) is a non-invasive and important tool for diagnosing fetal distress during pregnancy. To meet the needs of intelligent fetal hea...

Confidence-Based Annotation Of Brain Tumours In Ultrasound

Purpose: An investigation of the challenge of annotating discrete segmentations of brain tumours in ultrasound, with a focus on the issue of aleator...