Towards fair decentralized benchmarking of healthcare AI algorithms with the Federated Tumor Segmentation (FeTS) challenge.

Computational competitions are the standard for benchmarking medical image analysis algorithms, but they typically use small curated test datasets acquired at a few centers, leaving a gap to the reality of diverse multicentric patient data. To this end, the Federated Tumor Segmentation (FeTS) Challenge represents the paradigm for real-world algorithmic performance evaluation. The FeTS challenge is a competition to benchmark (i) federated learning aggregation algorithms and (ii) state-of-the-art segmentation algorithms, across multiple international sites.

Artificial intelligence education for radiographers, an evaluation of a UK postgraduate educational intervention using participatory action research: a pilot study.

Background: Artificial intelligence (AI)-enabled applications are increasingly being used in providing healthcare services, such as medical imaging support. Sufficient and appropriate education for medical imaging professionals is required for successful AI adoption. Although, currently, there are AI training programmes for radiologists, formal AI education for radiographers is lacking.

Enhancing MR image segmentation with realistic adversarial data augmentation.

The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impractical due to data sharing and privacy issues. To address this challenge, we propose AdvChain, a generic adversarial data augmentation framework, aiming at improving both the diversity and effectiveness of training data for medical image segmentation tasks.

MOOD 2020: A Public Benchmark for Out-of-Distribution Detection and Localization on Medical Images.

Detecting Out-of-Distribution (OoD) data is one of the greatest challenges in safe and robust deployment of machine learning algorithms in medicine. When the algorithms encounter cases that deviate from the distribution of the training data, they often produce incorrect and over-confident predictions. OoD detection algorithms aim to catch erroneous predictions in advance by analysing the data distribution and detecting potential instances of failure.

Anti-transfer learning for task invariance in convolutional neural networks for speech processing.

We introduce the novel concept of anti-transfer learning for speech processing with convolutional neural networks. While transfer learning assumes that the learning process for a target task will benefit from re-using representations learned for another task, anti-transfer avoids the learning of representations that have been learned for an orthogonal task, i.e.

A population-based phenome-wide association study of cardiac and aortic structure and function.

Differences in cardiac and aortic structure and function are associated with cardiovascular diseases and a wide range of other types of disease. Here we analyzed cardiovascular magnetic resonance images from a population-based study, the UK Biobank, using an automated machine-learning-based analysis pipeline. We report a comprehensive range of structural and functional phenotypes for the heart and aorta across 26,893 participants, and explore how these phenotypes vary according to sex, age and major cardiovascular risk factors.

Deep Learning for Cardiac Image Segmentation: A Review.

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound and major anatomical structures of interest (ventricles, atria, and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research.

Large-scale Quality Control of Cardiac Imaging in Population Studies: Application to UK Biobank.

In large population studies such as the UK Biobank (UKBB), quality control of the acquired images by visual assessment is unfeasible. In this paper, we apply a recently developed fully-automated quality control pipeline for cardiac MR (CMR) images to the first 19,265 short-axis (SA) cine stacks from the UKBB. We present the results for the three estimated quality metrics (heart coverage, inter-slice motion and image contrast in the cardiac region) as well as their potential associations with factors including acquisition details and subject-related phenotypes.

Explainable Anatomical Shape Analysis Through Deep Hierarchical Generative Models.

Quantification of anatomical shape changes currently relies on scalar global indexes which are largely insensitive to regional or asymmetric modifications. Accurate assessment of pathology-driven anatomical remodeling is a crucial step for the diagnosis and treatment of many conditions. Deep learning approaches have recently achieved wide success in the analysis of medical images, but they lack interpretability in the feature extraction and decision processes.

Learning-Based Quality Control for Cardiac MR Images.

The effectiveness of a cardiovascular magnetic resonance (CMR) scan depends on the ability of the operator to correctly tune the acquisition parameters to the subject being scanned and on the potential occurrence of imaging artifacts, such as cardiac and respiratory motion. In the clinical practice, a quality control step is performed by visual assessment of the acquired images; however, this procedure is strongly operator-dependent, cumbersome, and sometimes incompatible with the time constraints in clinical settings and large-scale studies. We propose a fast, fully automated, and learning-based quality control pipeline for CMR images, specifically for short-axis image stacks.

Automated cardiovascular magnetic resonance image analysis with fully convolutional networks.

Background: Cardiovascular resonance (CMR) imaging is a standard imaging modality for assessing cardiovascular diseases (CVDs), the leading cause of death globally. CMR enables accurate quantification of the cardiac chamber volume, ejection fraction and myocardial mass, providing information for diagnosis and monitoring of CVDs. However, for years, clinicians have been relying on manual approaches for CMR image analysis, which is time consuming and prone to subjective errors.

Large high-density lipoprotein particle number is independently associated with microvascular function in patients with well-controlled low-density lipoprotein concentration: A vasodilator stress magnetic resonance perfusion study.

Background: Abnormalities in total cholesterol, high-density lipoprotein, low-density lipoprotein, and triglycerides are associated with microvascular dysfunction. Recent studies suggest that lipid subfractions better predict atherogenic burden than a routine lipid panel. We sought to determine, whether lipid subfractions are more strongly associated with microvascular function and subclinical atherosclerosis, than conventional lipid measurements using vasodilator stress cardiovascular magnetic resonance (CMR).

A supervised learning approach for the robust detection of heart beat in plethysmographic data.

Wearable devices equipped with photoplethysmography (PPG) sensors are gaining an increased interest in the context of biometric signal monitoring within clinical, e-health and fitness settings. When used in everyday life and during exercise, PPG traces are heavily affected by artifacts originating from motion and from a non constant positioning and contact of the PPG sensor with the skin. Many algorithms have been developed for the estimation of heart-rate from photoplethysmography signals.

Fully-automated identification and segmentation of aortic lumen from fetal ultrasound images.

Intrauterine growth restriction (IUGR) is a fetal condition that has been linked to an increase in cardiovascular mortality in the adult life. IUGR induces cardiovascular remodeling, including a decrease in aortic intima-media thickness (aIMT) which can be evaluated using fetal ultrasound imaging, potentially improving IUGR assessment and cardiovascular risk management. A necessary step for aIMT quantification is the identification of a region-of-interest (ROI) containing the lumen.

Myocardial perfusion: near-automated evaluation from contrast-enhanced MR images obtained at rest and during vasodilator stress.

Purpose: To develop and validate a technique for near-automated definition of myocardial regions of interest suitable for perfusion evaluation during vasodilator stress cardiac magnetic resonance (MR) imaging.

Materials And Methods: The institutional review board approved the study protocol, and all patients provided informed consent. Image noise density distribution was used as a basis for endocardial and epicardial border detection combined with nonrigid registration.

Prosthetic component segmentation with blur compensation: a fast method for 3D fluoroscopy.

A new method for prosthetic component segmentation from fluoroscopic images is presented. The hybrid approach we propose combines diffusion filtering, region growing and level-set techniques without exploiting any a priori knowledge of the analyzed geometry. The method was evaluated on a synthetic dataset including 270 images of knee and hip prosthesis merged to real fluoroscopic data simulating different conditions of blurring and illumination gradient.