Search Results for author:
Found 30 papers, 9 papers with code
Effect of latent space distribution on the segmentation of images with multiple annotations
We propose the Generalized Probabilistic U-Net, which extends the Probabilistic U-Net by allowing more general forms of the Gaussian distribution as the latent space distribution that can better approximate the uncertainty in the reference segmentations.
Influence of uncertainty estimation techniques on false-positive reduction in liver lesion detection
We study the role played by features computed from neural network uncertainty estimates and shape-based features computed from binary predictions in reducing false positives in liver lesion detection by developing a classification-based post-processing step for different uncertainty estimation methods.
Generative Models for Reproducible Coronary Calcium Scoring
Interscan reproducibility was compared to clinical calcium scoring in radiotherapy treatment planning CTs of 1, 662 patients, each having two scans.
Explainable artificial intelligence (XAI) in deep learning-based medical image analysis
With an increase in deep learning-based methods, the call for explainability of such methods grows, especially in high-stakes decision making areas such as medical image analysis.
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.
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.
Liver segmentation and metastases detection in MR images using convolutional neural networks
Primary tumors have a high likelihood of developing metastases in the liver and early detection of these metastases is crucial for patient outcome.
Harmonization of diffusion MRI datasets with adaptive dictionary learning
Results show that the effect size of the four studied diffusion metrics is preserved while removing variability attributable to the scanner.
SMART tracking: Simultaneous anatomical imaging and real-time passive device tracking for MR-guided interventions
This approach was tested on tracking of five 0. 5 mm steel markers in an agarose phantom and on insertion of an MRI-compatible 20 Gauge titanium needle in ex vivo porcine tissue.
Automated characterization of noise distributions in diffusion MRI data
The proposed algorithms herein can estimate both parameters of the noise distribution, are robust to signal leakage artifacts and perform best when used on acquired noise maps.
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.
Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge
Segmentation methods had to be containerized and submitted to the challenge organizers.
Reducing variability in along-tract analysis with diffusion profile realignment
In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR).
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.
Direct Prediction of Cardiovascular Mortality from Low-dose Chest CT using Deep Learning
Chest CT scans made in lung cancer screening are suitable for identification of participants at risk of CVD.
A Deep Learning Framework for Unsupervised Affine and Deformable Image Registration
To circumvent the need for predefined examples, and thereby to increase convenience of training ConvNets for image registration, we propose the Deep Learning Image Registration (DLIR) framework for \textit{unsupervised} affine and deformable image registration.
Automatic, fast and robust characterization of noise distributions for diffusion MRI
Knowledge of the noise distribution in magnitude diffusion MRI images is the centerpiece to quantify uncertainties arising from the acquisition process.
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.
Direct and Real-Time Cardiovascular Risk Prediction
The results demonstrate that real-time quantification of CAC burden in chest CT without the need for segmentation of CAC is possible.
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 calcium scoring in low-dose chest CT using deep neural networks with dilated convolutions
On soft filter reconstructions, the method achieved F1 scores of 0. 89, 0. 89, 0. 67, and 0. 55 for coronary artery, thoracic aorta, aortic valve and mitral valve calcifications, respectively.
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.
End-to-End Unsupervised Deformable Image Registration with a Convolutional Neural Network
In this work we propose a deep learning network for deformable image registration (DIRNet).
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).
Automatic Segmentation of the Left Ventricle in Cardiac CT Angiography Using Convolutional Neural Network
Subsequently, to obtain the segmentation of the LV, voxel classification is performed within the defined bounding box using a CNN.
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.
Automatic segmentation of MR brain images with a convolutional neural network
Automatic segmentation in MR brain images is important for quantitative analysis in large-scale studies with images acquired at all ages.
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.
Assessment of algorithms for mitosis detection in breast cancer histopathology images
The proliferative activity of breast tumors, which is routinely estimated by counting of mitotic figures in hematoxylin and eosin stained histology sections, is considered to be one of the most important prognostic markers.
