Automatic 3D bi-ventricular segmentation of cardiac images by a shape-refined multi-task deep learning approach

Deep learning approaches have achieved state-of-the-art performance in cardiac magnetic resonance (CMR) image segmentation. However, most approaches have focused on learning image intensity features for segmentation, whereas the incorporation of anatomical shape priors has received less attention. In this paper, we combine a multi-task deep learning approach with atlas propagation to develop a shape-constrained bi-ventricular segmentation pipeline for short-axis CMR volumetric images. The pipeline first employs a fully convolutional network (FCN) that learns segmentation and landmark localisation tasks simultaneously. The architecture of the proposed FCN uses a 2.5D representation, thus combining the computational advantage of 2D FCNs networks and the capability of addressing 3D spatial consistency without compromising segmentation accuracy. Moreover, the refinement step is designed to explicitly enforce a shape constraint and improve segmentation quality. This step is effective for overcoming image artefacts (e.g. due to different breath-hold positions and large slice thickness), which preclude the creation of anatomically meaningful 3D cardiac shapes. The proposed pipeline is fully automated, due to network's ability to infer landmarks, which are then used downstream in the pipeline to initialise atlas propagation. We validate the pipeline on 1831 healthy subjects and 649 subjects with pulmonary hypertension. Extensive numerical experiments on the two datasets demonstrate that our proposed method is robust and capable of producing accurate, high-resolution and anatomically smooth bi-ventricular 3D models, despite the artefacts in input CMR volumes

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival

Biodegradable magnesium coronary stents: Material, design and fabrication

Biodegradable cardiovascular stents in magnesium (Mg) alloys constitute a promising option for a less intrusive treatment, due to their high compatibility with the body tissue and intrinsic dissolution in body fluids. The design and fabrication aspects of this medical device require an integrated approach considering different aspects such as mechanical properties, corrosion behaviour and biocompatibility. This work gathers and summarises a multidisciplinary work carried out by three different research teams for the design and fabrication of Mg stents. In particular, the paper discusses the design of the novel stent mesh, the deformability study of the Mg alloys for tubular raw material and laser microcutting for the realisation of the stent mesh. Although, the results are not fully validated as the device has not been fully tested, they show the feasibility of the used approaches, as the first prototype stents in Mg alloy were produced successfully. © 2013 Copyright Taylor and Francis Group, LLC

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. In this work, we propose a new interpretable deep learning model for shape analysis. In particular, we exploit deep generative networks to model a population of anatomical segmentations through a hierarchy of conditional latent variables. At the highest level of this hierarchy, a two-dimensional latent space is simultaneously optimised to discriminate distinct clinical conditions, enabling the direct visualisation of the classification space. Moreover, the anatomical variability encoded by this discriminative latent space can be visualised in the segmentation space thanks to the generative properties of the model, making the classification task transparent. This approach yielded high accuracy in the categorisation of healthy and remodelled left ventricles when tested on unseen segmentations from our own multi-centre dataset as well as in an external validation set, and on hippocampi from healthy controls and patients with Alzheimer's disease when tested on ADNI data. More importantly, it enabled the visualisation in three-dimensions of both global and regional anatomical features which better discriminate between the conditions under exam. The proposed approach scales effectively to large populations, facilitating high-throughput analysis of normal anatomy and pathology in large-scale studies of volumetric imaging

Epidemiology and natural history of central venous access device use and infusion pump function in the NO16966 trial

Background: Central venous access devices in fluoropyrimidine therapy are associated with complications; however, reliable data are lacking regarding their natural history, associated complications and infusion pump performance in patients with metastatic colorectal cancer.&lt;p&gt;&lt;/p&gt; Methods: We assessed device placement, use during treatment, associated clinical outcomes and infusion pump perfomance in the NO16966 trial.&lt;p&gt;&lt;/p&gt; Results: Device replacement was more common with FOLFOX-4 (5-fluorouracil (5-FU)+oxaliplatin) than XELOX (capecitabine+oxaliplatin) (14.1% vs 5.1%). Baseline device-associated events and post-baseline removal-/placement-related events occurred more frequently with FOLFOX-4 than XELOX (11.5% vs 2.4% and 8.5% vs 2.1%). Pump malfunctions, primarily infusion accelerations in 16% of patients, occurred within 1.6–4.3% of cycles. Fluoropyrimidine-associated grade 3/4 toxicity was increased in FOLFOX-4-treated patients experiencing a malfunction compared with those who did not (97 out of 155 vs 452 out of 825 patients), predominantly with increased grade 3/4 neutropenia (53.5% vs 39.8%). Febrile neutropenia rates were comparable between patient cohorts±malfunction. Efficacy outcomes were similar in patient cohorts±malfunction.&lt;p&gt;&lt;/p&gt; Conclusions: Central venous access device removal or replacement was common and more frequent in patients receiving FOLFOX-4. Pump malfunctions were also common and were associated with increased rates of grade 3/4 haematological adverse events. Oral fluoropyrimidine-based regimens may be preferable to infusional 5-FU based on these findings

Probiotic Administration Increases Amino Acid Absorption from Plant Protein: a Placebo-Controlled, Randomized, Double-Blind, Multicenter, Crossover Study

The fate of dietary protein in the gut is determined by microbial and host digestion and utilization. Fermentation of proteins generates bioactive molecules that have wide-ranging health effects on the host. The type of protein can affect amino acid absorption, with animal proteins generally being more efficiently absorbed compared with plant proteins. In contrast to animal proteins, most plant proteins, such as pea protein, are incomplete proteins. Pea protein is low in methionine and contains lower amounts of branched-chain amino acids (BCAAs), which play a crucial role in muscle health. We hypothesized that probiotic supplementation results in favorable changes in the gut microbiota, aiding the absorption of amino acids from plant proteins by the host. Fifteen physically active men (24.2 \ub1 5.0 years; 85.3 \ub1 12.9 kg; 178.0 \ub1 7.6 cm; 16.7 \ub1 5.8% body fat) co-ingested 20 g of pea protein with either AminoAlta\u2122, a multi-strain probiotic (5 billion CFU L. paracasei LP-DG\uae (CNCM I-1572) plus 5 billion CFU L. paracasei LPCS01 (DSM 26760), SOFAR S.p.A., Italy) or a placebo for 2 weeks in a randomized, double-blind, crossover design, separated by a 4-week washout period. Blood samples were taken at baseline and at 30-, 60-, 120-, and 180-min post-ingestion and analyzed for amino acid content. Probiotic administration significantly increased methionine, histidine, valine, leucine, isoleucine, tyrosine, total BCAA, and total EAA maximum concentrations (Cmax) and AUC without significantly changing the time to reach maximum concentrations. Probiotic supplementation can be an important nutritional strategy to improve post-prandial changes in blood amino acids and to overcome compositional shortcomings of plant proteins. ClinicalTrials.gov Identifier: ISRCTN3890378

Iroa: the international register of open abdomen. an international effort to better understand the open abdomen: call for participants

Actually the most common indications for Open Abdomen (OA) are trauma, abdominal sepsis, severe acute pancreatitis and more in general all those situations in which an intra-abdominal hypertension condition is present, in order to prevent the development of an abdominal compartment syndrome. The mortality and morbidity rate in patients undergone to OA procedures is still high. At present many studies have been published about the OA management and the progresses in survival rate of critically ill trauma and septic surgical patients. However several issues are still unclear and need more extensive studies. The definitions of indications, applications and methods to close the OA are still matter of debate. To overcome this lack of high level of evidence data about the OA indications, management, definitive closure and follow-up, the World Society of Emergency Surgery (WSES) promoted the International Register of Open Abdomen (IROA). The register will be held on a web platform (Clinical Registers (R)) through a dedicated web site: www. clinicalregisters. org. This will allow to all surgeons and physicians to participate from all around the world only by having a computer and a web connection. The IROA protocol has been approved by the coordinating center Ethical Committee (Papa Giovanni XXIII hospital, Bergamo, Italy).Actually the most common indications for Open Abdomen (OA) are trauma, abdominal sepsis, severe acute pancreatitis and more in general all those situations in which an intra-abdominal hypertension condition is present, in order to prevent the development103713sem informaçãosem informaçã

Assessing the Impact of Blood Pressure on Cardiac Function Using Interpretable Biomarkers and Variational Autoencoders

Maintaining good cardiac function for as long as possible is a major concern for healthcare systems worldwide and there is much interest in learning more about the impact of different risk factors on cardiac health. The aim of this study is to analyze the impact of systolic blood pressure (SBP) on cardiac function while preserving the interpretability of the model using known clinical biomarkers in a large cohort of the UK Biobank population. We propose a novel framework that combines deep learning based estimation of interpretable clinical biomarkers from cardiac cine MR data with a variational autoencoder (VAE). The VAE architecture integrates a regression loss in the latent space, which enables the progression of cardiac health with SBP to be learnt. Results on 3,600 subjects from the UK Biobank show that the proposed model allows us to gain important insight into the deterioration of cardiac function with increasing SBP, identify key interpretable factors involved in this process, and lastly exploit the model to understand patterns of positive and adverse adaptation of cardiac function