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Found 51 papers, 13 papers with code
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation
In this work, we introduce an active learning framework to reduce the number of CFD simulations required for the training of surrogate models, lowering the barriers to their deployment in new applications.
Learning Hemodynamic Scalar Fields on Coronary Artery Meshes: A Benchmark of Geometric Deep Learning Models
This study empirically analyzes various backends for predicting vFFR fields in coronary arteries as CFD surrogates, comparing six backends for learning hemodynamics on meshes using CFD solutions as ground truth.
Deep vectorised operators for pulsatile hemodynamics estimation in coronary arteries from a steady-state prior
This shows that deep vectorised operators are a powerful modelling tool for cardiovascular hemodynamics estimation in coronary arteries and beyond.
World of Forms: Deformable Geometric Templates for One-Shot Surface Meshing in Coronary CT Angiography
Deep learning-based medical image segmentation and surface mesh generation typically involve a sequential pipeline from image to segmentation to meshes, often requiring large training datasets while making limited use of prior geometric knowledge.
Physics-informed graph neural networks for flow field estimation in carotid arteries
This shows that physics-informed graph neural networks can be trained using 4D flow MRI data to estimate blood flow in unseen carotid artery geometries.
Neural Fields for Continuous Periodic Motion Estimation in 4D Cardiovascular Imaging
For a 3D + time imaging dataset, we optimize an implicit neural representation (INR) that represents a time-dependent velocity vector field (VVF).
Vestibular schwannoma growth prediction from longitudinal MRI by time conditioned neural fields
In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code.
Brain-Shift: Unsupervised Pseudo-Healthy Brain Synthesis for Novel Biomarker Extraction in Chronic Subdural Hematoma
The deformation fields derived from this process are utilized to extract biomarkers that quantify the shift in the brain due to cSDH.
Neural Fields for 3D Tracking of Anatomy and Surgical Instruments in Monocular Laparoscopic Video Clips
Where instrument and anatomy tracking have often been considered two separate problems, in this paper, we propose a method for joint tracking of all structures simultaneously.
Global Control for Local SO(3)-Equivariant Scale-Invariant Vessel Segmentation
To this end, we propose a combination of a global controller leveraging voxel mask segmentations to provide boundary conditions for vessels of interest to a local, iterative vessel segmentation model.
LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes
We propose LaB-GATr, a transfomer neural network with geometric tokenisation that can effectively learn with large-scale (bio-)medical surface and volume meshes through sequence compression and interpolation.
Quantifying white matter hyperintensity and brain volumes in heterogeneous clinical and low-field portable MRI
Brain atrophy and white matter hyperintensity (WMH) are critical neuroimaging features for ascertaining brain injury in cerebrovascular disease and multiple sclerosis.
SIRE: scale-invariant, rotation-equivariant estimation of artery orientations using graph neural networks
Hence, SIRE can be trained with arbitrarily oriented vessels with varying radii to generalise to vessels with a wide range of calibres and tortuosity.
CoNeS: Conditional neural fields with shift modulation for multi-sequence MRI translation
In this paper, we propose Conditional Neural fields with Shift modulation (CoNeS), a model that takes voxel coordinates as input and learns a representation of the target images for multi-sequence MRI translation.
Uncertainty-based quality assurance of carotid artery wall segmentation in black-blood MRI
The application of deep learning models to large-scale data sets requires means for automatic quality assurance.
The ACROBAT 2022 Challenge: Automatic Registration Of Breast Cancer Tissue
The alignment of tissue between histopathological whole-slide-images (WSI) is crucial for research and clinical applications.
Generative modeling of living cells with SO(3)-equivariant implicit neural representations
Finally, we show how microscopy images of living cells that correspond to our generated cell shapes can be synthesized using an image-to-image model.
Implicit Neural Representations for Modeling of Abdominal Aortic Aneurysm Progression
We represent the AAA wall over time as the zero-level set of a signed distance function (SDF), estimated by a multilayer perception that operates on space and time.
SE(3) symmetry lets graph neural networks learn arterial velocity estimation from small datasets
We demonstrate how to construct an SE(3)-equivariant GNN that is independent of the spatial orientation of the input mesh and show how this reduces the necessary amount of training data compared to a baseline neural network.
Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall
Computational fluid dynamics (CFD) is a valuable asset for patient-specific cardiovascular-disease diagnosis and prognosis, but its high computational demands hamper its adoption in practice.
Dynamic Depth-Supervised NeRF for Multi-View RGB-D Operating Room Images
Quantitatively, we evaluate view synthesis from an unseen camera position in terms of PSNR, SSIM and LPIPS for the colour channels and in MAE and error percentage for the estimated depth.
3D Human Pose Estimation in Multi-View Operating Room Videos Using Differentiable Camera Projections
Then, the detected joint locations are projected to 3D and fused over all camera views.
Going Off-Grid: Continuous Implicit Neural Representations for 3D Vascular Modeling
This allows us to model complex vascular structures with a representation that is implicit, continuous, light-weight, and easy to integrate with deep learning algorithms.
Implicit Neural Representations for Generative Modeling of Living Cell Shapes
Deep generative models for cell shape synthesis require a light-weight and flexible representation of the cell shape.
Super-Resolved Microbubble Localization in Single-Channel Ultrasound RF Signals Using Deep Learning
The resulting image shows an order-of-magnitude gain in axial resolution compared to a delay-and-sum reconstruction with unprocessed element data.
Deep Learning-Based Carotid Artery Vessel Wall Segmentation in Black-Blood MRI Using Anatomical Priors
Carotid artery vessel wall thickness measurement is an essential step in the monitoring of patients with atherosclerosis.
Mesh convolutional neural networks for wall shear stress estimation in 3D artery models
In this work, we propose to instead use mesh convolutional neural networks that directly operate on the same finite-element surface mesh as used in CFD.
Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT
Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction.
Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images
Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from.
Exploiting Clinically Available Delineations for CNN-based Segmentation in Radiotherapy Treatment Planning
We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns.
Automatic Online Quality Control of Synthetic CTs
We show that this uncertainty measure can be used for two kinds of online quality control.
Combined analysis of coronary arteries and the left ventricular myocardium in cardiac CT angiography for detection of patients with functionally significant stenosis
We combine our previous works for the analysis of the complete coronary artery tree and the LV myocardium: Coronary arteries are encoded by two disjoint convolutional autoencoders (CAEs) and the LV myocardium is characterized by a convolutional neural network (CNN) and a CAE.
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
In this work, we propose to use graph convolutional networks (GCNs) to predict the spatial location of vertices in a tubular surface mesh that segments the coronary artery lumen.
Adversarial optimization for joint registration and segmentation in prostate CT radiotherapy
Joint image registration and segmentation has long been an active area of research in medical imaging.
Deep learning analysis of coronary arteries in cardiac CT angiography for detection of patients requiring invasive coronary angiography
In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment.
Direct Automatic Coronary Calcium Scoring in Cardiac and Chest CT
To meet demands of the increasing interest in quantification of CAC, i. e. coronary calcium scoring, especially as an unrequested finding for screening and research, automatic methods have been proposed.
Generative adversarial networks and adversarial methods in biomedical image analysis
Generative adversarial networks (GANs) and other adversarial methods are based on a game-theoretical perspective on joint optimization of two neural networks as players in a game.
Automatic Segmentation of Thoracic Aorta Segments in Low-Dose Chest CT
Hence, we propose an automatic method to segment the ascending aorta, the aortic arch and the thoracic descending aorta in low-dose chest CT without contrast enhancement.
Coronary Artery Centerline Extraction in Cardiac CT Angiography Using a CNN-Based Orientation Classifier
Evaluation using 24 test images of the CAT08 challenge showed that extracted centerlines had an average overlap of 93. 7% with 96 manually annotated reference centerlines.
Towards increased trustworthiness of deep learning segmentation methods on cardiac MRI
Current state-of-the-art deep learning segmentation methods have not yet made a broad entrance into the clinical setting in spite of high demand for such automatic methods.
Improving Myocardium Segmentation in Cardiac CT Angiography using Spectral Information
We propose augmentation of the training data with virtual mono-energetic reconstructions from a spectral CT scanner which show different attenuation levels of the contrast agent.
CNN-based Landmark Detection in Cardiac CTA Scans
Under the assumption that patches close to a landmark can determine the landmark location more precisely than patches farther from it, only those patches that contain the landmark according to classification are used to determine the landmark location.
A Recurrent CNN for Automatic Detection and Classification of Coronary Artery Plaque and Stenosis in Coronary CT Angiography
The results demonstrate that automatic detection and classification of coronary artery plaque and stenosis are feasible.
Deep learning analysis of the myocardium in coronary CT angiography for identification of patients with functionally significant coronary artery stenosis
To identify patients with a functionally significant coronary artery stenosis, analysis is performed in several stages.
Automatic Segmentation and Disease Classification Using Cardiac Cine MR Images
We propose a fully automatic method for segmentation and disease classification using cardiac cine MR images.
Deep MR to CT Synthesis using Unpaired Data
MR-only radiotherapy treatment planning requires accurate MR-to-CT synthesis.
ConvNet-Based Localization of Anatomical Structures in 3D Medical Images
We propose a method for automatic localization of one or more anatomical structures in 3D medical images through detection of their presence in 2D image slices using a convolutional neural network (ConvNet).
Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease
Ten training and ten test CMR scans cropped to an ROI around the heart were provided in the MICCAI 2016 HVSMR challenge.
Deep Learning for Multi-Task Medical Image Segmentation in Multiple Modalities
The CNN therefore learns to identify the imaging modality, the visualised anatomical structures, and the tissue classes.
