The progress of deep convolutional neural networks has been successfully exploited in various real-time computer vision tasks such as image classification and segmentation. Owing to the development of computational units, availability of digital datasets, and improved performance of deep learning models, fully automati…
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48 results for “Quantitative Ultrasound”
Study decomposes uncertainty in HK-distribution parameter estimation for QUS.
problem Uncertainty in HK-distribution parameter estimation for quantitative ultrasound.
method Bayesian Neural Networks (BNNs) for parameter estimation and uncertainty decomposition.
result Decomposes total predictive uncertainty into epistemic and aleatoric components.
Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient-specific, operator-dependent, and machine-…
System guides freehand obstetric ultrasound probe movements.
problem Reducing operator expertise for standard plane acquisition.
method Artificial neural network trained on real scans.
result 88.8% accuracy for goal prediction, 90.9% for action prediction.
Neural network Kalman filtering improves 3D ultrasound object tracking.
problem Accurate and robust 3D positional estimation from 2D ultrasound data.
method Neural network training for out-of-plane offset estimation, combined with Kalman filtering.
result Mean error of 0.1mm for simulated data, 0.2mm for experimental data.
Microlocal analysis of an ultrasound transform with circular source and receiver trajectoriesmath.AP
In this article, we consider a generalized Radon transform that comes up in ultrasound reflection tomography. In our model, the ultrasound emitter and receiver move at a constant distance apart along a circle. We analyze the microlocal properties of the transform that arises from this model. As a consequence, we sh…
Robotic navigation improves with RL and ultrasound images.
problem Improving accuracy in robotic navigation for ultrasound-guided procedures.
method Combines deep Q-networks with ultrasound images and binary classifier.
result Significantly improved navigation accuracy compared to pure RL and SL.
Applying machine learning technologies, especially deep learning, into medical image segmentation is being widely studied because of its state-of-the-art performance and results. It can be a key step to provide a reliable basis for clinical diagnosis, such as 3D reconstruction of human tissues, image-guided interventio…
Preterm birth is the most common cause of neonatal death. Current diagnostic methods that assess the risk of preterm birth involve the collection of maternal characteristics and transvaginal ultrasound imaging conducted in the first and second trimester of pregnancy. Analysis of the ultrasound data is based on visual i…
Paper explores unsupervised learning for ultrasound image artifact removal.
problem Improving visual quality of ultrasound images from various artifacts.
method Inspired by optimal transport cycleGAN, unsupervised deep learning for artifact removal.
result Unsupervised learning method provides comparable results to supervised learning.
Medical ultrasound technology is widely used in routine clinical applications such as disease diagnosis and treatment as well as other applications like real-time monitoring of human tongue shapes and motions as visual feedback in second language training. Due to the low-contrast characteristic and noisy nature of ultr…
Ultrasound (US) imaging is based on the time-reversal principle, in which individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented as a delay-and-sum (DAS) beamformer, the image quality quickly degrades as the…
Contrast enhanced ultrasound is a radiation-free imaging modality which uses encapsulated gas microbubbles for improved visualization of the vascular bed deep within the tissue. It has recently been used to enable imaging with unprecedented subwavelength spatial resolution by relying on super-resolution techniques. A t…
Ultrasound (US) is the most widely used fetal imaging technique. However, US images have limited capture range, and suffer from view dependent artefacts such as acoustic shadows. Compounding of overlapping 3D US acquisitions into a high-resolution volume can extend the field of view and remove image artefacts, which is…
DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.
problem Imbalanced ultrasound modalities in diagnosing breast cancer.
method Integrates LUPI and MMD into a deep transfer learning framework.
result Outperforms state-of-the-art algorithms in BUS-based CAD.
Fast Approximate Time-Delay Estimation in Ultrasound Elastography Using Principal Component Analysiseess.IV
Time delay estimation (TDE) is a critical and challenging step in all ultrasound elastography methods. A growing number of TDE techniques require an approximate but robust and fast method to initialize solving for TDE. Herein, we present a fast method for calculating an approximate TDE between two radio frequency (RF) …
In ultrasound (US) imaging, individual channel RF measurements are back-propagated and accumulated to form an image after applying specific delays. While this time reversal is usually implemented using a hardware- or software-based delay-and-sum (DAS) beamformer, the performance of DAS decreases rapidly in situations w…
Single CNN removes multiple ultrasound artifacts.
problem Efficiently remove multiple ultrasound artifacts.
method OT-driven multi-domain unsupervised deep learning.
result Single neural network removes various artifacts.
Objective: Ultrasound elastography is gaining traction as an accessible and useful diagnostic tool for such things as cancer detection and differentiation and thyroid disease diagnostics. Unfortunately, state of the art shear wave imaging techniques, essential to promote this goal, are limited to high-end ultrasound ha…
In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an increasing demand to reconstruct high quality images from limited number of data. However, the existing solutions require either hardware changes or computationally expansive algorithms. To overcome these limitations, here we propose a novel d…
The automatic analysis of ultrasound sequences can substantially improve the efficiency of clinical diagnosis. In this work we present our attempt to automate the challenging task of measuring the vascular diameter of the fetal abdominal aorta from ultrasound images. We propose a neural network architecture consisting …
One of the biggest challenges for deep learning algorithms in medical image analysis is the indiscriminate mixing of image properties, e.g. artifacts and anatomy. These entangled image properties lead to a semantically redundant feature encoding for the relevant task and thus lead to poor generalization of deep learnin…
Improved AI lung ultrasound segmentation using expert confidence values.
problem Label uncertainty in lung ultrasound due to subjective interpretation by radiologists.
method Designing a data annotation protocol capturing expert confidence, training AI on binarized labels with confidence thresholds.
result Improved AI segmentation and better clinical outcomes (e.g., S/F oxygenation ratio estimation, patient readmission prediction).
Self-supervised method enhances ultrasound images without needing clean targets.
problem Multiplicative speckle, acquisition blur, and scanner artifacts hamper ultrasound interpretation.
method Physics-guided degradation model trained on rotated/cropped patches with synthesized inputs.
result Achieves highest PSNR/SSIM across Gaussian and speckle noise levels, with significant improvements in heavy noise conditions.
In this paper we present a new method for motion tracking of tumors in liver ultrasound image sequences. Our algorithm has two main steps. In the first step, we apply mean shift algorithm with multiple features to estimate the center of the target in each frame. Target in the first frame is defined using an ellipse. Ed…
Deep learning maps tongue movements to speech sounds for voiceless individuals.
problem Developing silent speech interfaces for individuals without a larynx.
method Hybrid spatio-temporal 3D convolutions and feature shuffling for formant estimation and tracking from ultrasound tongue images.
result Best model achieves R-squared of 99.96% for vowel formant regression.
Switchable Deep Beamformereess.IV
A switchable deep beamformer enables versatile image processing.
problem Training and storing separate beamformers for each application.
method Switchable deep beamformer using Adaptive Instance Normalization (AdaIN) layers.
result Single network can produce various image processing outputs.
Image post-processing is used in clinical-grade ultrasound scanners to improve image quality (e.g., reduce speckle noise and enhance contrast). These post-processing techniques vary across manufacturers and are generally kept proprietary, which presents a challenge for researchers looking to match current clinical-grad…
PLIs improve classifier performance by fine-tuning latent representations.
problem Difficult interpretation of high-dimensional latent representations in neural networks.
method Back-propagation of manual changes to low-dimensional embeddings using t-distributed stochastic neighbourhood embeddings.
result Manual separation of class clusters in latent space enhances classifier performance.
In portable, three dimensional, and ultra-fast ultrasound imaging systems, there is an increasing demand for the reconstruction of high quality images from a limited number of radio-frequency (RF) measurements due to receiver (Rx) or transmit (Xmit) event sub-sampling. However, due to the presence of side lobe artifact…
Incorporating human domain knowledge for breast tumor diagnosis is challenging, since shape, boundary, curvature, intensity, or other common medical priors vary significantly across patients and cannot be employed. This work proposes a new approach for integrating visual saliency into a deep learning model for breast t…
Ultrasound (US) is one of the most commonly used imaging modalities in both diagnosis and surgical interventions due to its low-cost, safety, and non-invasive characteristic. US image segmentation is currently a unique challenge because of the presence of speckle noise. As manual segmentation requires considerable effo…
A benchmark evaluates ioUS-to-MR synthesis methods for brain tumor surgery.
problem Difficult interpretation of ioUS images for brain tumor surgery.
method Six generators trained under four inference regimes and two targets on public data.
result SynDiff-2.5D best preserved downstream segmentation (U_Dice=0.55).
Volumetric analysis of brain ventricle (BV) structure is a key tool in the study of central nervous system development in embryonic mice. High-frequency ultrasound (HFU) is the only non-invasive, real-time modality available for rapid volumetric imaging of embryos in utero. However, manual segmentation of the BV from H…
Ultrasound elastography is used to estimate the mechanical properties of the tissue by monitoring its response to an internal or external force. Different levels of deformation are obtained from different tissue types depending on their mechanical properties, where stiffer tissues deform less. Given two radio frequency…
This paper presents a novel approach for detection of liver abnormalities in an automated manner using ultrasound images. For this purpose, we have implemented a machine learning model that can not only generate labels (normal and abnormal) for a given ultrasound image but it can also detect when its prediction is like…
Prostate motion modelling using biomechanically-trained deep neural networks on unstructured nodescs.LG
Deep neural networks predict prostate motion from MR images.
problem Predicting prostate motion during ultrasound-guided interventions.
method Biomechanically-trained deep neural networks on unstructured nodes.
result Trained networks yield near real-time inference with 0.017 mm error.
Noninvasive MR-guided focused ultrasound (MRgFUS) treatments are promising alternatives to the surgical removal of malignant tumors. A significant challenge is assessing the viability of treated tissue during and immediately after MRgFUS procedures. Current clinical assessment uses the nonperfused volume (NPV) biomarke…
IntraVascular UltraSound (IVUS) is one of the most effective imaging modalities that provides assistance to experts in order to diagnose and treat cardiovascular diseases. We address a central problem in IVUS image analysis with Fully Convolutional Network (FCN): automatically delineate the lumen and media-adventitia b…
Physics-informed neural network identifies and characterizes surface cracks in metals.
problem Identifying and characterizing surface-breaking cracks in metals using ultrasound.
method Physics-informed neural network (PINN) trained with ultrasonic surface wave data and adaptive activation functions.
result PINN accurately estimates the speed of sound and identifies crack locations in metals.
Efficient method for training deep learning models with human validation and statistical analysis.
problem Challenges in labeling medical images for deep learning, including time and cost.
method Four-step method using automated data and human visual checks for iterative refinement and statistical validation.
result Initial model accuracy improved from 92% to 98% with statistical validation.
In this paper, we give a proof of the quantitative Morse theorem stated by {Y. Yomdin} in \cite{Y1}. The proof is based on the quantitative Sard theorem, the quantitative inverse function theorem and the quantitative Morse lemma.
Qlib aims to integrate AI into quantitative investment.
problem Challenges in applying AI to quantitative investment.
method Design and develop Qlib to accommodate AI-driven workflow.
result Qlib realizes the potential of AI technologies in quantitative investment.
The book explores essential stats and psychology for quantitative trading.
problem Developing a quantitative trading system.
method Logical progression through articles on statistics, quantitative trading, and psychology.
result Essential elements for quantitative trading systems.
Proves quantitative Alexandrov theorem for capillary surfaces.
problem Proving a quantitative version of the Alexandrov theorem for capillary hypersurfaces.
method Quantitative analysis of Montiel-Ros-type argument.
result Generalizes Julin-Niinikoski's result to capillary case.
Proves upper bound on filling radius for manifolds with positive scalar curvature.
problem Bounding the filling radius of manifolds with positive scalar curvature.
method Quantitative operator K-theory and index theory.
result Proves a quantitative upper bound on the filling radius.
The paper develops quantitative estimates for holomorphic sections over bounded domains.
problem Establishing precise inequalities for holomorphic sections over bounded domains.
method Develops Sobolev-type inequalities and applies them to holomorphic sections of Hermitian vector bundles.
result Quantitative Carleman-type estimates for holomorphic sections are derived, improving on previous non-quantitative results.
Study proves quantitative results for isoperimetric problem outside convex bodies in the plane.
problem Quantitative estimates for the relative isoperimetric problem outside convex bodies in the plane.
method Flow approach and Łojasiewicz estimates to prove quantitative stability for minimizers.
result Explicit constants and optimal exponents/rates for Łojasiewicz estimates and rates of convergence for gradient flow.