RibSeg v2: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

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T1-weighted structural MRI is widely used to measure brain morphometry (e.g., cortical thickness and subcortical volumes). Accelerated scans as fast as one minute or less are now available but it is unclear if they are adequate for quantitative morphometry ...

Segmented meso-voids from a tomography scan of preforms impregnated under capillary dominated conditions, combined to 3D scans of the initial prefrom dry state. ...

The high computational costs of deep convolutional neural networks hinder their deployment in real-world applications, including pulmonary nodule detection from CT scans where large 3D image sizes amplify the issue. This paper presents a novel 3D method to ...

Open international challenges are becoming the de facto standard for assessing computer vision and image analysis algorithms. In recent years, new methods have extended the reach of pulmonary airway segmentation that is closer to the limit of image resolut ...

BackgroundInterstitial lung diseases (ILD), such as idiopathic pulmonary fibrosis (IPF) and non-specific interstitial pneumonia (NSIP), and chronic obstructive pulmonary disease (COPD) are severe, progressive pulmonary disorders with a poor prognosis. Prom ...

Objectives To evaluate the performance of automatic deep learning (DL) algorithm for size, mass, and volume measurements in predicting prognosis of lung adenocarcinoma (LUAD) and compared with manual measurements. ...

Motivated by alternating game-play in two-player games, we study an altenating variant of the Online Linear Optimization (OLO). In alternating OLO, a learner at each round t ∈[n] selects a vector xt and then an adversary selects a cost-vector ct ∈[−1,1]n. ...

Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild. In this paper, we report on a quantitative study with 139 industrial practitioners. We analyze at ...

Medipix4 is the latest member in the Medipix/Timepix family of pixel detector chips aimed at high rate spectroscopic X-ray imaging using high-Z materials. The chip address the limitations of conventional hybrid pixel detectors for X-ray imaging. Its predec ...

Despite the successes of deep neural networks on many challenging vision tasks, they often fail to generalize to new test domains that are not distributed identically to the training data. The domain adaptation becomes more challenging for cross-modality m ...

Background Coercion in psychiatry is a controversial issue. Identifying its predictors and their interaction using traditional statistical methods is difficult, given the large number of variables involved. The purpose of this study was to use machine-lear ...

Background and objective: Compared to the conventional magnetization-prepared rapid gradient-echo imaging (MPRAGE) MRI sequence, the specialized magnetization prepared 2 rapid acquisition gradient echoes (MP2RAGE) shows a higher brain tissue and lesion con ...

Recovering the distribution from multi-echo magnetic resonance (MR) signals is challenging but has high potential as it provides biomarkers characterizing the tissue micro-structure, such as the myelin water fraction (MWF). In this work, we propose to comb ...

Objective: Stereotactic ablative radiotherapy (SABR) is being increasingly used as a non-invasive treatment for early-stage non-small cell lung cancer (NSCLC). A non-invasive method to estimate treatment outcomes in these patients would be valuable, especi ...

Radiomics has shown promising results in several medical studies, yet it suffers from a limited discrimination and informative capability as well as a high variation and correlation with the tomographic scanner types, pixel spacing, acquisition protocol, a ...

Reactive microgliosis is an important pathological component of neuroinflammation and has been implicated in a wide range of brain diseases including brain tumors, multiple sclerosis, Parkinson's disease, Alzheimer's disease, and schizophrenia. Mapping rea ...

Despite being a powerful medical imaging technique which does not emit any ionizing radiation, magnetic resonance imaging (MRI) always had the major problem of long scanning times that can take up to an hour depending on the application. It also requires u ...

Medical experts commonly use imaging including Computed Tomography (CT), Positron-Emission Tomography (PET) and Magnetic Resonance Imaging (MRI) for diagnosis or to plan a surgery. These scans give a highly detailed representation of the patient anatomy, b ...

Oxygenation is an important marker in many clinical settings, e.g. diagnosing ischaemic brain injuries in preterm infants or determining treatment effectiveness in cancer patients. Despite significant efforts to determine the oxygenation state of the human ...

Tissue engineering technologies are new and promising techniques in fat tissue reconstruction. However, to assess their efficacy before any clinical application, in vivo experiments are mandatory. This study assesses whether microcomputed tomography (CT) s ...

RibSeg v2: A Large-Scale Benchmark for Rib Labeling and Anatomical Centerline Extraction

Chattez avec Graph Search

Brain morphometry in older adults with and without dementia using extremely rapid structural scans

3D representation of meso-voids location and morphology within the fibrous pore space of three carbon fabrics

Efficient Lung Nodule Detection via 3D Deep Learning with Shifted Convolutions

Multi-site, Multi-domain Airway Tree Modeling

Deep learning diagnostic and severity-stratification for interstitial lung diseases and chronic obstructive pulmonary disease in digital lung auscultations and ultrasonography: clinical protocol for an observational case-control study

Prognostic impact of deep learning-based quantification in clinical stage 0-I lung adenocarcinoma

Alternation makes the adversary weaker in two-player games

Machine Learning Security in Industry: A Quantitative Survey

Medipix4, a high granularity four sides buttable pixel readout chip for high resolution spectroscopic X-ray imaging at rates compatible with medical CT scans

Self-Attentive Spatial Adaptive Normalization for Cross-Modality Domain Adaptation

Predicting involuntary hospitalization in psychiatry: A machine learning investigation

MPRAGE to MP2RAGE UNI translation via generative adversarial network improves the automatic tissue and lesion segmentation in multiple sclerosis patients

Model-informed machine learning for multi-component T2 relaxometry

The use of texture-based radiomics CT analysis to predict outcomes in early-stage non-small cell lung cancer treated with stereotactic ablative radiotherapy

Neural network training for cross-protocol radiomic feature standardization in computed tomography

Extra-axonal restricted diffusion as an in-vivo marker of reactive microglia

Data-driven Measurement Designs for Magnetic Resonance Imaging

Holographic Visualisation and Interaction of Fused CT, PET and MRI Volumetric Medical Imaging Data Using Dedicated Remote GPGPU Ray Casting

Time-resolved Single-photon Detector Arrays for High Resolution Near-infrared Optical Tomography

Microcomputed Tomography Technique for In Vivo Three-Dimensional Fat Tissue Volume Evaluation After Polymer Injection