Classification of CT brain images based on deep learning networks

While Computerised Tomography (CT) may have been the first imag-ing tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimers disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great ex-tent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early di-agnosis of Alzheimers disease. Towards this end, three categories of CT images (N=285) are clustered into three groups, which are AD, Lesion (e.g. tumour) and Normal ageing. In addition, considering the character-istics of this collection with larger thickness along the direction of depth (z) (∼3-5mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification ac-curacy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, Lesion and Normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6+-1:10, 86.3+-1:04, 85.2+-1:60, 83.1+-0:35 for 2D CNN, 2D SIFT, 2DKAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information rooted in both 2D slices and 3D blocks of CT images and an elaborated hand-crated approach of 3D KAZE

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

A VIRTUAL BRAIN FOR STEREOTACTIC PLANNING AND SUPPORTING NEUROSURGERY

International audienceVisualization has becoming a powerful assistive technology for neurosurgery. This paperintroduces a system for stereotactic neurosurgical planning and support. Using visualizationtechnology the system reconstructs and displays a 3D model of the interior structure of thepatient’s brain. Thus the surgeons can plan for surgery using a computer model. Markerregistration is used to create the mapping between the patient’s head and the brain modelreconstructed in the computer. During the operation a robot arm is used as a navigator to locatethe pre-defined incision site and the orientation of incision route. When the robot arm locates atthe pre-defined site on the patient’s head, it is fixed. Various medical instruments can be installedon the tip of the robot arm. The surgeon can insert a medical instrument into the pre-defined siteof the patient’s head, and the surgery can be implemented successfully with the help of thissystem. Using a virtual environment his system can also be used to teach and train new surgeons

Thermometric analysis of blood metabolites in ICU patients

Real-time monitoring of patient’s blood metabolites, such as glucose and lactate, could potentially improve surgery and recovery outcomes for patients in surgical and intensive care units. Our enzyme thermometric biosensor which is based on flow injected calorimetric determination of immobilized enzyme reaction is capable of performing continuous, fast, and quantitative analysis of metabolites using whole blood. A key technical advantage the assay affords is the ability to use unpretreated whole blood. In this article, the enzyme thermometric biosensor was used, for the first time, to determine glucose and lactate concentrations in the blood of ICU patients. The linear detection range for glucose was 0.5–30 mM and 0.25–12 mM for lactate, using a 20 μL sample volume. A maximum sampling rate of 15 measurements per hour was achieved using venous blood samples, which corresponds to a 4-min measurement interval. In order to validate the accuracy of the results, a comparative analysis between the thermometric biosensor and the clinically applied instrument (LifeScan’s OneTouch®) which is based on disposable dry chemical reaction was performed using samples from 33 patients. The results showed a good correlation between the two methods for both glucose (r = 0.843, p < 0.0001) and lactate (r = 0.78, p = 0.0105). The ability to monitor metabolite levels and trends on a clinically relevant timescale of 5 min is critical for intensive monitoring of ICP and operative patients