TIST-Net: style transfer in dynamic contrast enhanced MRI using spatial and temporal information.

Objective.Training deep learning models for image registration or segmentation of dynamic contrast enhanced (DCE) MRI data is challenging. This is mainly due to the wide variations in contrast enhancement within and between patients. To train a model effectively, a large dataset is needed, but acquiring it is expensive and time consuming. Instead, style transfer can be used to generate new images from existing images. In this study, our objective is to develop a style transfer method that incorporates spatio-temporal information to either add or remove contrast enhancement from an existing image.Approach.We propose a temporal image-to-image style transfer network (TIST-Net), consisting of an auto-encoder combined with convolutional long short-term memory networks. This enables disentanglement of the content and style latent spaces of the time series data, using spatio-temporal information to learn and predict key structures. To generate new images, we use deformable and adaptive convolutions which allow fine grained control over the combination of the content and style latent spaces. We evaluate our method, using popular metrics and a previously proposed contrast weighted structural similarity index measure. We also perform a clinical evaluation, where experts are asked to rank images generated by multiple methods.Main Results.Our model achieves state-of-the-art performance on three datasets (kidney, prostate and uterus) achieving an SSIM of 0.91 ± 0.03, 0.73 ± 0.04, 0.88 ± 0.04 respectively when performing style transfer between a non-enhanced image and a contrast-enhanced image. Similarly, SSIM results for style transfer from a contrast-enhanced image to a non-enhanced image were 0.89 ± 0.03, 0.82 ± 0.03, 0.87 ± 0.03. In the clinical evaluation, our method was ranked consistently higher than other approaches.Significance.TIST-Net can be used to generate new DCE-MRI data from existing images. In future, this may improve models for tasks such as image registration or segmentation by allowing small training datasets to be expanded.

Prevalence of microbial nosocomial infections in the resuscitation unit of the University Hospital of Annaba-Algeria / Prévalence des infections nosocomiales d'origine microbienne au service de réanimation du Centre hospitalo-universitaire de Annaba (Algérie)

Nosocomial infections constitute a significant public health problem but are poorly controlled in our health structures, especially those associated with resuscitation care. The first objective of this study was to identify the different microbial strains present in different biological samples taken from patients staying in the resuscitation unit of the Annaba University Hospital Center. The second objective was to assess the antimicrobial sensitivity of isolated microbes from the patients' samples, to determine the risk factors, the most incriminated microbial agents in nosocomial infections. During the study period from January 2013 to December 2016, we collected 1,151 biological samples from 1,938 patients admitted to Resuscitation Medical Service. The samples were subjected to different microbiological analyses. Our results showed that over 59% of the collected samples were microbiologically positive. The identified species include Candida albicans (115 cases) and Candida.sp (81 cases). The Gram-negative bacterial strains found in the samples included Acinetobacter baumannii (108 cases), Klebssiella pneumoniae (99 cases) Pseudomonas aeruginosa (79 cases), and Escherichia coli (73 cases). Gram positive bacteria included Staphylococcus aureus (94 cases) and Enterococcus faecalis (53 cases). The antibiogram analyses showed significant antibiotic resistance reaching 93.75% for ampicillin, but sensitivity to colistin reaching 81.81%. Moreover, the fungal strains are represented by the genus albicans, showing a significant resistance to antifungals, reaching 80% with miconazole. Conclusion. The nosocomial infections in the medical unit were caused by the candida genus and multi-resistant bacteria to various antibiotics and antifungals. The most important factor associated with these infections was the use of medical devices.

Contexte: Les infections nosocomiales sont un grand problème de santé publique, encore méconnu et mal maîtrisé au sein des structures sanitaires. Objectif: Le but de notre étude était d'identifier les différentes souches microbiennes présentes dans les prélèvements obtenus de patients séjournant à l'unité de réanimation du Centre hospitalo-universitaire d'Annaba. Nous avons également évalué leurs sensibilités aux antimicrobiens, et pour déterminer les facteurs de risque, les agents infectieux les plus incriminés ainsi que la sensibilité aux traitements proposés afin de prévenir et/ou traiter les infections nosocomiales. Matériel et méthode: C'est une étude rétrospective de janvier 2013 à décembre 2016, analysant les différents prélèvements microbiologiques effectués au service de réanimation médicale d'Annaba-Algérie. Durant cette période d'étude, 1 151 prélèvements ont été effectués sur les 1 938 patients admis. Ces prélèvements ont été soumis à différentes analyses microbiologiques. Résultats: Les résultats montrent que plus de 59 % des prélèvements contenaient des microbes. En effet, nous avons identifié Candida albicans dans 115, et Candida sp. dans 81 prélèvements. Les bactéries Gram négatif incluent l'Acinetobacter baumannii dans 108, Klbessiella pneumoniae dans 99, Pseudomonas aeruginosa dans 79, et Escherichia coli dans 73 prélèvements. Pour les bactéries Gram positif, nous avons isolé Staphylococcus aureus dans 94, et Enterococcus faecalis dans 53 prélèvements. Les antibiogrammes montrent une importante résistance aux différents antibiotiques, avec une résistance 93,75 % pour l'ampicilline, et une sensibilité à la colistine de 81,81 %. Les souches fongiques représentées par le genre albicans affichent aussi une résistance aux antifongiques qui atteignent 80 % pour la miconazole. Conclusion: Les infections nosocomiales dans l'unité de réanimation médicale du Centre hospitalo-universitaire (CHU) d'Annaba sont dominées par le genre candida et des bactéries multi résistantes à différents antibiotiques et antifongiques. Le port d'un dispositif médical semble favoriser les infections nosocomiales.

In vivo textural and morphometric analysis of placental development in healthy & growth-restricted pregnancies using magnetic resonance imaging.

BACKGROUND: The objective of this study was to characterize structural changes in the healthy in vivo placenta by applying morphometric and textural analysis using magnetic resonance imaging (MRI), and to explore features that may be able to distinguish placental insufficiency in fetal growth restriction (FGR). METHODS: Women with healthy pregnancies or pregnancies complicated by FGR underwent MRI between 20 and 40 weeks gestation. Measures of placental morphometry (volume, elongation, depth) and digital texture (voxel-wise geometric and signal-intensity analysis) were calculated from T2W MR images. RESULTS: We studied 66 pregnant women (32 healthy controls, 34 FGR); during the study period, placentas undergo significant increases in size; signal intensity remains relatively constant, however there is increasing variation in spatial arrangements, suggestive of progressive microstructural heterogeneity. In FGR, placental size is smaller, with great homogeneity of signal intensity and spatial arrangements. CONCLUSION: We report quantitative textural and morphometric changes in the in vivo placenta in healthy controls over the second half of pregnancy. These MRI features demonstrate important differences in placental development in the setting of placental insufficiency that relate to onset and severity of FGR, as well as neonatal outcome.

The challenge of cerebral magnetic resonance imaging in neonates: A new method using mathematical morphology for the segmentation of structures including diffuse excessive high signal intensities.

Preterm birth is a multifactorial condition associated with increased morbidity and mortality. Diffuse excessive high signal intensity (DEHSI) has been recently described on T2-weighted MR sequences in this population and thought to be associated with neuropathologies. To date, no robust and reproducible method to assess the presence of white matter hyperintensities has been developed, perhaps explaining the current controversy over their prognostic value. The aim of this paper is to propose a new semi-automated framework to detect DEHSI on neonatal brain MR images having a particular pattern due to the physiological lack of complete myelination of the white matter. A novel method for semi- automatic segmentation of neonatal brain structures and DEHSI, based on mathematical morphology and on max-tree representations of the images is thus described. It is a mandatory first step to identify and clinically assess homogeneous cohorts of neonates for DEHSI and/or volume of any other segmented structures. Implemented in a user-friendly interface, the method makes it straightforward to select relevant markers of structures to be segmented, and if needed, apply eventually manual corrections. This method responds to the increasing need for providing medical experts with semi-automatic tools for image analysis, and overcomes the limitations of visual analysis alone, prone to subjectivity and variability. Experimental results demonstrate that the method is accurate, with excellent reproducibility and with very few manual corrections needed. Although the method was intended initially for images acquired at 1.5T, which corresponds to the usual clinical practice, preliminary results on images acquired at 3T suggest that the proposed approach can be generalized.

In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome.

PURPOSE: To investigate the ability of three-dimensional (3D) MRI placental shape and textural features to predict fetal growth restriction (FGR) and birth weight (BW) for both healthy and FGR fetuses. MATERIALS AND METHODS: We recruited two groups of pregnant volunteers between 18 and 39 weeks of gestation; 46 healthy subjects and 34 FGR. Both groups underwent fetal MR imaging on a 1.5 Tesla GE scanner using an eight-channel receiver coil. We acquired T2-weighted images on either the coronal or the axial plane to obtain MR volumes with a slice thickness of either 4 or 8 mm covering the full placenta. Placental shape features (volume, thickness, elongation) were combined with textural features; first order textural features (mean, variance, kurtosis, and skewness of placental gray levels), as well as, textural features computed on the gray level co-occurrence and run-length matrices characterizing placental homogeneity, symmetry, and coarseness. The features were used in two machine learning frameworks to predict FGR and BW. RESULTS: The proposed machine-learning based method using shape and textural features identified FGR pregnancies with 86% accuracy, 77% precision and 86% recall. BW estimations were 0.3 ± 13.4% (mean percentage error ± standard error) for healthy fetuses and -2.6 ± 15.9% for FGR. CONCLUSION: The proposed FGR identification and BW estimation methods using in utero placental shape and textural features computed on 3D MR images demonstrated high accuracy in our healthy and high-risk cohorts. Future studies to assess the evolution of each feature with regard to placental development are currently underway. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018;47:449-458.

Unsupervised fetal cortical surface parcellation.

At the core of many neuro-imaging studies, atlas-based brain parcellations are used for example to study normal brain evolution across the lifespan. These atlases rely on the assumption that the same anatomical features are present on all subjects to be studied and that these features are stable enough to allow meaningful comparisons between different brain surfaces and structures These methods, however, often fail when applied to fetal MRI data, due to the lack of consistent anatomical features present across gestation. This paper presents a novel surface-based fetal cortical parcellation framework which attempts to circumvent the lack of consistent anatomical features by proposing a brain parcellation scheme that is based solely on learned geometrical features. A mesh signature incorporating both extrinsic and intrinsic geometrical features is proposed and used in a clustering scheme to define a parcellation of the fetal brain. This parcellation is then learned using a Random Forest (RF) based learning approach and then further refined in an alpha-expansion graph-cut scheme. Based on the votes obtained by the RF inference procedure, a probability map is computed and used as a data term in the graph-cut procedure. The smoothness term is defined by learning a transition matrix based on the dihedral angles of the faces. Qualitative and quantitative results on a cohort of both healthy and high-risk fetuses are presented. Both visual and quantitative assessments show good results demonstrating a reliable method for fetal brain data and the possibility of obtaining a parcellation of the fetal cortical surfaces using only geometrical features.

Segmentation of embryonic and fetal 3D ultrasound images based on pixel intensity distributions and shape priors.

This paper presents a novel variational segmentation framework combining shape priors and parametric intensity distribution modeling for extracting the fetal envelope on 3D obstetric ultrasound images. To overcome issues related to poor image quality and missing boundaries, we inject three types of information in the segmentation process: tissue-specific parametric modeling of pixel intensities, a shape prior for the fetal envelope and a shape model of the fetus' back. The shape prior is encoded with Legendre moments and used to constraint the evolution of a level-set function. The back model is used to post-process the segmented fetal envelope. Results are presented on 3D ultrasound data and compared to a set of manual segmentations. The robustness of the algorithm is studied, and both visual and quantitative comparisons show satisfactory results obtained by the proposed method on the tested dataset.