Search Results for author:
Found 35 papers, 8 papers with code
Deep Learning-Based Detection of Motion-Affected k-Space Lines for T2*-Weighted MRI
T2*-weighted gradient echo MR imaging is strongly impacted by subject head motion due to motion-related changes in B0 inhomogeneities.
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI
This new model, coined masked autoencoder for medical imaging (MAEMI) is trained on two non-contrast enhanced MRI sequences, aiming at lesion detection without the need for intravenous injection of contrast media and temporal image acquisition.
Fully transformer-based biomarker prediction from colorectal cancer histology: a large-scale multicentric study
Methods: In this study, we developed a new fully transformer-based pipeline for end-to-end biomarker prediction from pathology slides.
Automated Quality Controlled Analysis of 2D Phase Contrast Cardiovascular Magnetic Resonance Imaging
Flow analysis carried out using phase contrast cardiac magnetic resonance imaging (PC-CMR) enables the quantification of important parameters that are used in the assessment of cardiovascular function.
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CT
This makes high-quality GANs useful for unsupervised anomaly detection in medical imaging.
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.
What do we learn? Debunking the Myth of Unsupervised Outlier Detection
We have found that state-of-the-art (SOTA) AEs are either unable to constrain the latent manifold and allow reconstruction of abnormal patterns, or they are failing to accurately restore the inputs from their latent distribution, resulting in blurred or misaligned reconstructions.
A Deep Learning-based Integrated Framework for Quality-aware Undersampled Cine Cardiac MRI Reconstruction and Analysis
Cine cardiac magnetic resonance (CMR) imaging is considered the gold standard for cardiac function evaluation.
The Impact of Domain Shift on Left and Right Ventricle Segmentation in Short Axis Cardiac MR Images
Our dataset contains short axis images from 4 different MR scanners and 3 different pathology groups.
Quality-aware Cine Cardiac MRI Reconstruction and Analysis from Undersampled k-space Data
The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters.
The Pitfalls of Sample Selection: A Case Study on Lung Nodule Classification
Using publicly available data to determine the performance of methodological contributions is important as it facilitates reproducibility and allows scrutiny of the published results.
The Effect of the Loss on Generalization: Empirical Study on Synthetic Lung Nodule Data
Convolutional Neural Networks (CNNs) are widely used for image classification in a variety of fields, including medical imaging.
Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review
Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is commonly used to visualize and quantify left atrial (LA) scars.
AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs
Left atrial (LA) segmentation from late gadolinium enhanced magnetic resonance imaging (LGE MRI) is a crucial step needed for planning the treatment of atrial fibrillation.
Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information
As the target domain could be unknown, we randomly generate a modality vector for the target modality in the style transfer stage, to simulate the domain shift for unknown domains.
AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information
In this work, we develop a new framework, namely AtrialJSQnet, where LA segmentation, scar projection onto the LA surface, and scar quantification are performed simultaneously in an end-to-end style.
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.
Joint Left Atrial Segmentation and Scar Quantification Based on a DNN with Spatial Encoding and Shape Attention
Compared to conventional binary label based loss, the proposed SE loss can reduce noisy patches in the resulting segmentation, which is commonly seen for deep learning-based methods.
Deep Learning Based Detection and Correction of Cardiac MR Motion Artefacts During Reconstruction for High-Quality Segmentation
In this paper, we discuss the implications of image motion artefacts on cardiac MR segmentation and compare a variety of approaches for jointly correcting for artefacts and segmenting the cardiac cavity.
A Topological Loss Function for Deep-Learning based Image Segmentation using Persistent Homology
We show that the incorporation of the prior knowledge of the topology of this anatomy improves the resulting segmentations in terms of both the topological accuracy and the Dice coefficient.
dAUTOMAP: decomposing AUTOMAP to achieve scalability and enhance performance
AUTOMAP is a promising generalized reconstruction approach, however, it is not scalable and hence the practicality is limited.
Model-Based and Data-Driven Strategies in Medical Image Computing
With the availability of large amounts of imaging data and machine learning (in particular deep learning) techniques, data-driven approaches have become more widespread for use in different tasks in reconstruction, analysis and interpretation.
Global and Local Interpretability for Cardiac MRI Classification
Deep learning methods for classifying medical images have demonstrated impressive accuracy in a wide range of tasks but often these models are hard to interpret, limiting their applicability in clinical practice.
Detection and Correction of Cardiac MR Motion Artefacts during Reconstruction from K-space
In this paper, we propose a method to automatically detect and correct motion-related artefacts in CMR acquisitions during reconstruction from k-space data.
Mechanically Powered Motion Imaging Phantoms: Proof of Concept
Motion imaging phantoms are expensive, bulky and difficult to transport and set-up.
FastReg: Fast Non-Rigid Registration via Accelerated Optimisation on the Manifold of Diffeomorphisms
We present an implementation of a new approach to diffeomorphic non-rigid registration of medical images.
Explicit topological priors for deep-learning based image segmentation using persistent homology
We present a novel method to explicitly incorporate topological prior knowledge into deep learning based segmentation, which is, to our knowledge, the first work to do so.
Magnetic Resonance Fingerprinting using Recurrent Neural Networks
Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative magnetic resonance imaging that allows simultaneous measurement of multiple tissue properties in a single, time-efficient acquisition.
Automatic CNN-based detection of cardiac MR motion artefacts using k-space data augmentation and curriculum learning
Due to the high number of good quality images compared to the relatively low number of images with motion artefacts, we propose a novel data augmentation scheme based on synthetic artefact creation in k-space.
Deep Learning using K-space Based Data Augmentation for Automated Cardiac MR Motion Artefact Detection
As this is a highly imbalanced classification problem (due to the high number of good quality images compared to the low number of images with motion artefacts), we propose a novel k-space based training data augmentation approach in order to address this problem.
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.
Learning associations between clinical information and motion-based descriptors using a large scale MR-derived cardiac motion atlas
The availability of large scale databases containing imaging and non-imaging data, such as the UK Biobank, represents an opportunity to improve our understanding of healthy and diseased bodily function.
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.
Extracting 3D Vascular Structures from Microscopy Images using Convolutional Recurrent Networks
Vasculature is known to be of key biological significance, especially in the study of cancer.
Pieces-of-parts for supervoxel segmentation with global context: Application to DCE-MRI tumour delineation
Rectal tumour segmentation in dynamic contrast-enhanced MRI (DCE-MRI) is a challenging task, and an automated and consistent method would be highly desirable to improve the modelling and prediction of patient outcomes from tissue contrast enhancement characteristics - particularly in routine clinical practice.
