Can we rely on histopathological results for the diagnosis of prosthetic joint infection?

Background: Histopathology is one of the diagnostic criteria for prosthetic joint infection (PJI) proposed by all academic societies. The aim of this study was to compare histopathological and microbiological results from samples taken intraoperatively at the same site in patients with suspected or proven PJI.

Patients And Methods: We conducted a monocenter retrospective study including all patients having undergone surgery from 2007 to 2015 with suspected or proven PJI.

Chlamydia psittaci endocarditis: A case report and literature review.

Objectives: Blood-culture-negative infective endocarditis (BCNE) is found in 2 to 48% of cases of infective endocarditis (IE) (Houpikian and Raoult, 2005) [1].IE and vertebral osteomyelitis due to Chlamydia sp. are difficult to diagnose.

Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning.

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches.

Influence of inputs for bone lesion segmentation in longitudinal F-FDG PET/CT imaging studies.

In metastatic breast cancer, bone metastases are prevalent and associated with multiple complications. Assessing their response to treatment is therefore crucial. Most deep learning methods segment or detect lesions on a single acquisition while only a few focus on longitudinal studies.

Deformable image registration with deep network priors: a study on longitudinal PET images.

This paper proposes a novel approach for the longitudinal registration of PET imaging acquired for the monitoring of patients with metastatic breast cancer. Unlike with other image analysis tasks, the use of deep learning (DL) has not significantly improved the performance of image registration. With this work, we propose a new registration approach to bridge the performance gap between conventional and DL-based methods: medical image registration method regularized by architecture (MIRRBA).

Automatic Segmentation of Metastatic Breast Cancer Lesions on F-FDG PET/CT Longitudinal Acquisitions for Treatment Response Assessment.

Metastatic breast cancer patients receive lifelong medication and are regularly monitored for disease progression. The aim of this work was to (1) propose networks to segment breast cancer metastatic lesions on longitudinal whole-body PET/CT and (2) extract imaging biomarkers from the segmentations and evaluate their potential to determine treatment response. Baseline and follow-up PET/CT images of 60 patients from the EPICUREseinmeta study were used to train two deep-learning models to segment breast cancer metastatic lesions: One for baseline images and one for follow-up images.

Combining Superpixels and Deep Learning Approaches to Segment Active Organs in Metastatic Breast Cancer PET Images.

Semi-automatic measurements are performed on FDG PET-CT images to monitor the evolution of metastatic sites in the clinical follow-up of metastatic breast cancer patients. Apart from being time-consuming and prone to subjective approximation, semi-automatic tools cannot make the difference between cancerous regions and active organs, presenting a high FDG uptake.In this work, we combine a deep learning-based approach with a superpixel segmentation method to segment the main active organs (brain, heart, bladder) from full-body PET images.

Deep learning approaches for bone and bone lesion segmentation on 18FDG PET/CT imaging in the context of metastatic breast cancer.

FDG PET/CT imaging is commonly used in diagnosis and follow-up of metastatic breast cancer, but its quantitative analysis is complicated by the number and location heterogeneity of metastatic lesions. Considering that bones are the most common location among metastatic sites, this work aims to compare different approaches to segment the bones and bone metastatic lesions in breast cancer.Two deep learning methods based on U-Net were developed and trained to segment either both bones and bone lesions or bone lesions alone on PET/CT images.

Automatic classification and removal of structured physiological noise for resting state functional connectivity MRI analysis.

Resting state functional magnetic resonance imaging is used to study how brain regions are functionally connected by measuring temporal correlation of the fMRI signals, when a subject is at rest. Sparse dictionary learning is used to estimate a dictionary of resting state networks by decomposing the whole brain signals into several temporal features (atoms), each being shared by a set of voxels associated to a network. Recently, we proposed and validated a new method entitled Sparsity-based Analysis of Reliable K-hubness (SPARK), suggesting that connector hubs of brain networks participating in inter-network communication can be identified by counting the number of atoms involved in each voxel (sparse number k).

Characteristics of Enterobacter cloacae prosthetic joint infections.

Objectives: Enterobacter cloacae prosthetic joint infections (PJI) are rare and poorly documented.

Patients And Methods: We conducted a retrospective and monocentric study in an orthopedic unit supporting complex bone and joint infections. Between 2012 and 2016 we collected background, clinical, biological, and microbiological data from 20 patients presenting with prosthetic joint infection and positive for E.

Shortening of the Short Refractory Periods in Short QT Syndrome.

Background: Diagnosis of short QT syndrome (SQTS) remains difficult in case of borderline QT values as often found in normal populations. Whether some shortening of refractory periods (RP) may help in differentiating SQTS from normal subjects is unknown.

Methods And Results: Atrial and right ventricular RP at the apex and right ventricular outflow tract as determined during standard electrophysiological study were compared between 16 SQTS patients (QTc 324±24 ms) and 15 controls with similar clinical characteristics (QTc 417±32 ms).