A deep learning based approach to classification of CT brain images

This study explores the applicability of the state of the art of deep learning convolutional neural network (CNN) to the classification of CT brain images, aiming at bring images into clinical applications. Towards this end, three categories are clustered, which contains subjects’ data with either Alzheimer’s disease (AD) or lesion (e.g. tumour) or normal ageing. Specifically, due to the characteristics of CT brain images with larger thickness along depth (z) direction (~5mm), both 2D and 3D CNN are employed in this research. The fusion is therefore conducted based on both 2D CT images along axial direction and 3D segmented blocks with the accuracy rates are 88.8%, 76.7% and 95% for classes of AD, lesion and normal respectively, leading to an average of 86.8%

An enhanced deep learning architecture for classification of Tuberculosis types from CT lung images

In this work, an enhanced ResNet deep learning network, depth-ResNet, has been developed to classify the five types of Tuberculosis (TB) lung CT images. Depth-ResNet takes 3D CT images as a whole and processes the volumatic blocks along depth directions. It builds on the ResNet-50 model to obtain 2D features on each frame and injects depth information at each process block. As a result, the averaged accuracy for classification is 71.60% for depth-ResNet and 68.59% for ResNet. The datasets are collected from the ImageCLEF 2018 competition with 1008 training data in total, where the top reported accuracy was 42.27%

The state of the art of medical imaging technology: from creation to archive and back.

Medical imaging has learnt itself well into modern medicine and revolutionized medical industry in the last 30 years. Stemming from the discovery of X-ray by Nobel laureate Wilhelm Roentgen, radiology was born, leading to the creation of large quantities of digital images as opposed to film-based medium. While this rich supply of images provides immeasurable information that would otherwise not be possible to obtain, medical images pose great challenges in archiving them safe from corrupted, lost and misuse, retrievable from databases of huge sizes with varying forms of metadata, and reusable when new tools for data mining and new media for data storing become available. This paper provides a summative account on the creation of medical imaging tomography, the development of image archiving systems and the innovation from the existing acquired image data pools. The focus of this paper is on content-based image retrieval (CBIR), in particular, for 3D images, which is exemplified by our developed online e-learning system, MIRAGE, home to a repository of medical images with variety of domains and different dimensions. In terms of novelties, the facilities of CBIR for 3D images coupled with image annotation in a fully automatic fashion have been developed and implemented in the system, resonating with future versatile, flexible and sustainable medical image databases that can reap new innovations

3D CBIR with sparse coding for image-guided neurosurgery

This research takes an application-specific approach to investigate, extend and implement the state of the art in the fields of both visual information retrieval and machine learning, bridging the gap between theoretical models and real world applications. During an image-guided neurosurgery, path planning remains the foremost and hence the most important step to perform an operation and ensures the maximum resection of an intended target and minimum sacrifice of health tissues. In this investigation, the technique of content-based image retrieval (CBIR) coupled with machine learning algorithms are exploited in designing a computer aided path planning system (CAP) to assist junior doctors in planning surgical paths while sustaining the highest precision. Specifically, after evaluation of approaches of sparse coding and K-means in constructing a codebook, the model of sparse codes of 3D SIFT has been furthered and thereafter employed for retrieving, The novelty of this work lies in the fact that not only the existing algorithms for 2D images have been successfully extended into 3D space, leading to promising results, but also the application of CBIR, that is mainly in a research realm, to a clinical sector can be achieved by the integration with machine learning techniques. Comparison with the other four popular existing methods is also conducted, which demonstrates that with the implementation of sparse coding, all methods give better retrieval results than without while constituting the codebook, implying the significant contribution of machine learning techniques

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

Optimization of cool roof and night ventilation in office buildings: A case study in Xiamen, China

Increasing roof albedo (using a “cool” roof) and night ventilation are passive cooling technologies that can reduce the cooling loads in buildings, but existing studies have not comprehensively explored the potential benefits of integrating these two technologies. This study combines an experiment in the summer and transition seasons with an annual simulation so as to evaluate the thermal performance, energy savings and thermal comfort improvement that could be obtained by coupling a cool roof with night ventilation. A holistic approach integrating sensitivity analysis and multi-objective optimization is developed to explore key design parameters (roof albedo, night ventilation air change rate, roof insulation level and internal thermal mass level) and optimal design options for the combined application of the cool roof and night ventilation. The proposed approach is validated and demonstrated through studies on a six-storey office building in Xiamen, a cooling-dominated city in southeast China. Simulations show that combining a cool roof with night ventilation can significantly decrease the annual cooling energy consumption by 27% compared to using a black roof without night ventilation and by 13% compared to using a cool roof without night ventilation. Roof albedo is the most influential parameter for both building energy performance and indoor thermal comfort. Optimal use of the cool roof and night ventilation can reduce the annual cooling energy use by 28% during occupied hours when air-conditioners are on and reduce the uncomfortable time slightly during occupied hours when air-conditioners are off

Magnetic fields of the W4 superbubble

Superbubbles and supershells are the channels for transferring mass and energy from the Galactic disk to the halo. Magnetic fields are believed to play a vital role in their evolution. We study the radio continuum and polarized emission properties of the W4 superbubble to determine its magnetic field strength. New sensitive radio continuum observations were made at 6 cm, 11 cm, and 21 cm. The total intensity measurements were used to derive the radio spectrum of the W4 superbubble. The linear polarization data were analysed to determine the magnetic field properties within the bubble shells. The observations show a multi-shell structure of the W4 superbubble. A flat radio continuum spectrum that stems from optically thin thermal emission is derived from 1.4 GHz to 4.8 GHz. By fitting a passive Faraday screen model and considering the filling factor fne , we obtain the thermal electron density ne = 1.0/\sqrt{fne} (\pm5%) cm^-3 and the strength of the line-of-sight component of the magnetic field B// = -5.0/\sqrt{fne} (\pm10%) {\mu}G (i.e. pointing away from us) within the western shell of the W4 superbubble. When the known tilted geometry of the W4 superbubble is considered, the total magnetic field Btot in its western shell is greater than 12 {\mu}G. The electron density and the magnetic field are lower and weaker in the high-latitude parts of the superbubble. The rotation measure is found to be positive in the eastern shell but negative in the western shell of the W4 superbubble, which is consistent with the case that the magnetic field in the Perseus arm is lifted up from the plane towards high latitudes. The magnetic field strength and the electron density we derived for the W4 superbubble are important parameters for evolution models of superbubbles breaking out of the Galactic plane.Comment: 13 pages, 8 figures, accepted for publication in Astronomy & Astrophysic