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Found 18 papers, 9 papers with code
Fourier-Net+: Leveraging Band-Limited Representation for Efficient 3D Medical Image Registration
Instead of directly predicting a full-resolution displacement field, our Fourier-Net learns a low-dimensional representation of the displacement field in the band-limited Fourier domain which our model-driven decoder converts to a full-resolution displacement field in the spatial domain.
Automatic retrieval of corresponding US views in longitudinal examinations
Skeletal muscle atrophy is a common occurrence in critically ill patients in the intensive care unit (ICU) who spend long periods in bed.
Echo from noise: synthetic ultrasound image generation using diffusion models for real image segmentation
We propose a novel pipeline for the generation of synthetic ultrasound images via Denoising Diffusion Probabilistic Models (DDPMs) guided by cardiac semantic label maps.
Feature-Conditioned Cascaded Video Diffusion Models for Precise Echocardiogram Synthesis
So far, video generation has only been possible by providing input data that is as rich as the output data, e. g., image sequence plus conditioning in, video out.
A Machine Learning Case Study for AI-empowered echocardiography of Intensive Care Unit Patients in low- and middle-income countries
We present a Machine Learning (ML) study case to illustrate the challenges of clinical translation for a real-time AI-empowered echocardiography system with data of ICU patients in LMICs.
Efficient Pix2Vox++ for 3D Cardiac Reconstruction from 2D echo views
Accurate geometric quantification of the human heart is a key step in the diagnosis of numerous cardiac diseases, and in the management of cardiac patients.
Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography
We propose a new method to automatically contour the left ventricle on 2D echocardiographic images.
Placenta Segmentation in Ultrasound Imaging: Addressing Sources of Uncertainty and Limited Field-of-View
Automatic segmentation of the placenta in fetal ultrasound (US) is challenging due to the (i) high diversity of placenta appearance, (ii) the restricted quality in US resulting in highly variable reference annotations, and (iii) the limited field-of-view of US prohibiting whole placenta assessment at late gestation.
AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography
In this work we propose for the first time an AI approach for deriving advanced biomarkers of systolic and diastolic LV function from 2-D echocardiography based on segmentations of the full cardiac cycle.
B-line Detection in Lung Ultrasound Videos: Cartesian vs Polar Representation
As a result, we hypothesize that a polar representation may be more adequate for automatic image analysis of these images.
Automatic Detection of B-lines in Lung Ultrasound Videos From Severe Dengue Patients
Lung ultrasound (LUS) imaging is used to assess lung abnormalities, including the presence of B-line artefacts due to fluid leakage into the lungs caused by a variety of diseases.
Mutual Information-based Disentangled Neural Networks for Classifying Unseen Categories in Different Domains: Application to Fetal Ultrasound Imaging
To address this problem, we propose Mutual Information-based Disentangled Neural Networks (MIDNet), which extract generalizable categorical features to transfer knowledge to unseen categories in a target domain.
Screen Tracking for Clinical Translation of Live Ultrasound Image Analysis Methods
Ultrasound (US) imaging is one of the most commonly used non-invasive imaging techniques.
Confident Head Circumference Measurement from Ultrasound with Real-time Feedback for Sonographers
Manual estimation of fetal Head Circumference (HC) from Ultrasound (US) is a key biometric for monitoring the healthy development of fetuses.
Mechanically Powered Motion Imaging Phantoms: Proof of Concept
Motion imaging phantoms are expensive, bulky and difficult to transport and set-up.
Weakly Supervised Estimation of Shadow Confidence Maps in Fetal Ultrasound Imaging
Our method is more consistent than human annotation, and outperforms the state-of-the-art quantitatively in shadow segmentation and qualitatively in confidence estimation of shadow regions.
Weakly Supervised Localisation for Fetal Ultrasound Images
This paper addresses the task of detecting and localising fetal anatomical regions in 2D ultrasound images, where only image-level labels are present at training, i. e. without any localisation or segmentation information.
Adapted and Oversegmenting Graphs: Application to Geometric Deep Learning
From the adapted graph, we also propose the computation of a dual graph, which inherits the saliency measure from the adapted graph, and whose edges run along image features, hence producing an oversegmenting graph.
