Self-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction

In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. However, successfully learning these features requires a large amount of manually annotated data, which is expensive to acquire and limited by the available resources of expert image analysts. Therefore, unsupervised, weakly-supervised and self-supervised feature learning techniques receive a lot of attention, which aim to utilise the vast amount of available data, while at the same time avoid or substantially reduce the effort of manual annotation. In this paper, we propose a novel way for training a cardiac MR image segmentation network, in which features are learnt in a self-supervised manner by predicting anatomical positions. The anatomical positions serve as a supervisory signal and do not require extra manual annotation. We demonstrate that this seemingly simple task provides a strong signal for feature learning and with self-supervised learning, we achieve a high segmentation accuracy that is better than or comparable to a U-net trained from scratch, especially at a small data setting. When only five annotated subjects are available, the proposed method improves the mean Dice metric from 0.811 to 0.852 for short-axis image segmentation, compared to the baseline U-net

State of the art in bile analysis in forensic toxicology

AbstractIn forensic toxicology, alternative matrices to blood are useful in case of limited, unavailable or unusable blood sample, suspected postmortem redistribution or long drug intake-to-sampling interval. The present article provides an update on the state of knowledge for the use of bile in forensic toxicology, through a review of the Medline literature from 1970 to May 2015. Bile physiology and technical aspects of analysis (sampling, storage, sample preparation and analytical methods) are reported, to highlight specificities and consequences from an analytical and interpretative point of view. A table summarizes cause of death and quantification in bile and blood of 133 compounds from more than 200 case reports, providing a useful tool for forensic physicians and toxicologists involved in interpreting bile analysis. Qualitative and quantitative interpretation is discussed. As bile/blood concentration ratios are high for numerous molecules or metabolites, bile is a matrix of choice for screening when blood concentrations are low or non-detectable: e.g., cases of weak exposure or long intake-to-death interval. Quantitative applications have been little investigated, but small molecules with low bile/blood concentration ratios seem to be good candidates for quantitative bile-based interpretation. Further experimental data on the mechanism and properties of biliary extraction of xenobiotics of forensic interest are required to improve quantitative interpretation

eTRIKS Analytical Environment: A Modular High Performance Framework for Medical Data Analysis

Translational research is quickly becoming a science driven by big data. Improving patient care, developing personalized therapies and new drugs depend increasingly on an organization's ability to rapidly and intelligently leverage complex molecular and clinical data from a variety of large-scale partner and public sources. As analysing these large-scale datasets becomes computationally increasingly expensive, traditional analytical engines are struggling to provide a timely answer to the questions that biomedical scientists are asking. Designing such a framework is developing for a moving target as the very nature of biomedical research based on big data requires an environment capable of adapting quickly and efficiently in response to evolving questions. The resulting framework consequently must be scalable in face of large amounts of data, flexible, efficient and resilient to failure. In this paper we design the eTRIKS Analytical Environment (eAE), a scalable and modular framework for the efficient management and analysis of large scale medical data, in particular the massive amounts of data produced by high-throughput technologies. We particularly discuss how we design the eAE as a modular and efficient framework enabling us to add new components or replace old ones easily. We further elaborate on its use for a set of challenging big data use cases in medicine and drug discovery

High dimensional biological data retrieval optimization with NoSQL technology.

Background High-throughput transcriptomic data generated by microarray experiments is the most abundant and frequently stored kind of data currently used in translational medicine studies. Although microarray data is supported in data warehouses such as tranSMART, when querying relational databases for hundreds of different patient gene expression records queries are slow due to poor performance. Non-relational data models, such as the key-value model implemented in NoSQL databases, hold promise to be more performant solutions. Our motivation is to improve the performance of the tranSMART data warehouse with a view to supporting Next Generation Sequencing data. Results In this paper we introduce a new data model better suited for high-dimensional data storage and querying, optimized for database scalability and performance. We have designed a key-value pair data model to support faster queries over large-scale microarray data and implemented the model using HBase, an implementation of Google's BigTable storage system. An experimental performance comparison was carried out against the traditional relational data model implemented in both MySQL Cluster and MongoDB, using a large publicly available transcriptomic data set taken from NCBI GEO concerning Multiple Myeloma. Our new key-value data model implemented on HBase exhibits an average 5.24-fold increase in high-dimensional biological data query performance compared to the relational model implemented on MySQL Cluster, and an average 6.47-fold increase on query performance on MongoDB. Conclusions The performance evaluation found that the new key-value data model, in particular its implementation in HBase, outperforms the relational model currently implemented in tranSMART. We propose that NoSQL technology holds great promise for large-scale data management, in particular for high-dimensional biological data such as that demonstrated in the performance evaluation described in this paper. We aim to use this new data model as a basis for migrating tranSMART's implementation to a more scalable solution for Big Data

Head orientation benefit to speech intelligibility in noise for cochlear implant users and in realistic listening conditions

Cochlear implant (CI) users suffer from elevated speech-reception thresholds and may rely on lip reading. Traditional measures of spatial release from masking quantify speech-reception-threshold improvement with azimuthal separation of target speaker and interferers and with the listener facing the target speaker. Substantial benefits of orienting the head away from the target speaker were predicted by a model of spatial release from masking. Audio-only and audio-visual speech-reception thresholds in normal-hearing (NH) listeners and bilateral and unilateral CI users confirmed model predictions of this head-orientation benefit. The benefit ranged 2–5 dB for a modest 30� orientation that did not affect the lip-reading benefit. NH listeners’ and CI users’ lip-reading benefit measured 3 and 5 dB, respectively. A head-orientation benefit of �2 dB was also both predicted and observed in NH listeners in realistic simulations of a restaurant listening environment. Exploiting the benefit of head orientation is thus a robust hearing tactic that would benefit both NH listeners and CI users in noisy listening conditions

Genetic and molecular characterization of bud dormancy in apple: deciphering candidate gene roles in dormancy regulation.

Dormancy is an adaptive mechanism that enables plants to survive unfavorable climatic conditions, for example during winter, and allows flowering to occur only when the conditions are more permissive, typically in spring

An overview of the mid-infrared spectro-interferometer MATISSE: science, concept, and current status

MATISSE is the second-generation mid-infrared spectrograph and imager for the Very Large Telescope Interferometer (VLTI) at Paranal. This new interferometric instrument will allow significant advances by opening new avenues in various fundamental research fields: studying the planet-forming region of disks around young stellar objects, understanding the surface structures and mass loss phenomena affecting evolved stars, and probing the environments of black holes in active galactic nuclei. As a first breakthrough, MATISSE will enlarge the spectral domain of current optical interferometers by offering the L and M bands in addition to the N band. This will open a wide wavelength domain, ranging from 2.8 to 13 um, exploring angular scales as small as 3 mas (L band) / 10 mas (N band). As a second breakthrough, MATISSE will allow mid-infrared imaging - closure-phase aperture-synthesis imaging - with up to four Unit Telescopes (UT) or Auxiliary Telescopes (AT) of the VLTI. Moreover, MATISSE will offer a spectral resolution range from R ~ 30 to R ~ 5000. Here, we present one of the main science objectives, the study of protoplanetary disks, that has driven the instrument design and motivated several VLTI upgrades (GRA4MAT and NAOMI). We introduce the physical concept of MATISSE including a description of the signal on the detectors and an evaluation of the expected performances. We also discuss the current status of the MATISSE instrument, which is entering its testing phase, and the foreseen schedule for the next two years that will lead to the first light at Paranal.Comment: SPIE Astronomical Telescopes and Instrumentation conference, June 2016, 11 pages, 6 Figure

Safety and diagnostic yield of renal biopsy in the intensive care unit

Purpose Renal biopsy (RB) is occasionally performed in critically ill patients. The safety and impact of RB in this setting have not been reported. Methods A 10-year (2000–2009) retrospective multicentre study was conducted in ten French intensive care units (ICU) on patients who underwent RB during their management. Medical files were retrieved for data analysis. Results Seventy-seven patients underwent an RB of which 68 (88 %) were on a native kidney and 9 (12 %) on a transplanted kidney. Percutaneous ultrasound-guided RB was used in most cases (87 %). Fifty-seven per cent of the patients were on mechanical ventilation at the time of RB. RB-related complications occurred in 17 (22 %) patients, two were graded as severe (requirement for kidney embolization, eventually successful). In 35 (51 %) non-transplanted patients, RB established a specific diagnosis other than acute tubular necrosis (ATN), which was diagnosed in only 18 % of patients. In the remaining patients, only non-specific lesions were observed. Therapeutic modifications followed RB in 14 (21 %) non-transplanted patients. Presence of signs of systemic disease involving the renal tract, occurrence of renal failure before hospital admission, and absence of any factor usually associated with ATN significantly predicted the presence of a specific diagnosis at RB other than ATN. Conclusions In this cohort, the contribution of RB to diagnosis and treatment was undeniable, but at the expense of frequent adverse events although most of them were not considered severe