Probabilistic Spatial Regression using a Deep Fully Convolutional Neural Network

Probabilistic predictions are often preferred in computer vision problems because they can provide a confidence of the predicted value. The recent dominant model for computer vision problems, the convolutional neural network, produces probabilistic output for classification and segmentation problems. But probabilistic regression using neural networks is not well defined. In this work, we present a novel fully convolutional neural network capable of producing a spatial probabilistic distribution for localizing image landmarks. We have introduced a new network layer and a novel loss function for the network to produce a two-dimensional probability map. The proposed network has been used in a novel framework to localize vertebral corners for lateral cervical Xray images. The framework has been evaluated on a dataset of 172 images consisting 797 vertebrae and 3,188 vertebral corners. The proposed framework has demonstrated promising performance in localizing vertebral corners, with a relative improvement of 38% over the previous state-of-the-art

Global Localization and Orientation of the Cervical Spine in X-ray Imaging

Injuries in cervical spine X-ray images are often missed by emergency physicians. Many of these missing injuries cause further complications. Automated analysis of the images has the potential to reduce the chance of missing injuries. Towards this goal, this paper proposes an automatic localization of the spinal column in cervical spine X-ray images. The framework employs a random classification forest algorithm with a kernel density estimation-based voting accumulation method to localize the spinal column and to detect the orientation. The algorithm has been evaluated with 90 emergency room X-ray images and has achieved an average detection accuracy of 91% and an orientation error of 3.6◦. The framework can be used to narrow the search area for other advanced injury detection systems

Improving an Active Shape Model with Random Classification Forest for Segmentation of Cervical Vertebrae

X-ray is a common modality for diagnosing cervical vertebrae injuries. Many injuries are missed by emergency physicians which later causes life threatening complications. Computer aided analysis of X-ray images has the potential to detect missed injuries. Segmentation of the vertebrae is a crucial step towards automatic injury detection system. Active shape model (ASM) is one of the most successful and popular method for vertebrae segmentation. In this work, we propose a new ASM search method based on random classification forest and a kernel density estimation-based prediction technique. The proposed method have been tested on a dataset of 90 emergency room X-ray images containing 450 vertebrae and outperformed the classical Mahalanobis distancebased ASM search and also the regression forest-based method

Hough Forest-based Corner Detection for Cervical Spine Radiographs

The cervical spine (neck region) is highly sensitive to trauma related injuries, which must be analysed carefully by emergency physicians. In this work, we propose a Hough Forest-based corner detection method for cervical spine radiographs, as a first step towards a computer-aided diagnostic tool. We propose a novel patch-based model based on two-stage supervised learning (classification and regression) to estimate the corners of cervical vertebral bodies. Our method is evaluated using 106 cervical x-ray images consisting of 530 vertebrae and 2120 corners, which have been demarcated manually by an expert radiographer. The results show promising performance of the proposed algorithm, with a lowest median error of 1.98 m

Patch-based Corner Detection for Cervical Vertebrae in X-ray Images

Corners hold vital information about size, shape and morphology of a vertebra in an x-ray image, and recent literature [1, 2] has shown promising performance for detecting vertebral corners using a Hough forest-based architecture. To provide spatial context, this method generates a set of 12 patches around a vertebra and uses a machine learning approach to predict corners of a vertebral body through a voting process. In this paper, we extend this framework in terms of patch generation and prediction methods. During patch generation, the square region of interest has been replaced with data-driven rectangular and trapezoidal region of interest which better aligns the patches to the vertebral body geometry, resulting in more discriminative feature vectors. The corner estimation or the prediction stage has been improved by utilising more efficient voting process using a single kernel density estimation. In addition, advanced and more complex feature vectors are introduced. We also present a thorough evaluation of the framework with different patch generation methods, forest training mechanisms and prediction methods. In order to compare the performance of this framework with a more general method, a novel multi-scale Harris corner detector-based approach is introduced that incorporates a spatial prior through a naive Bayes method. All these methods have been tested on a dataset of 90 X-ray images and achieved an average corner localization error of 2.01 mm, representing a 33% improvement in localisation accuracy compared to the previous state-of-the-art method [2]

Partial protection of verapamil against gentamicin nephrotoxicity in rats

A number of male rats (40) were used to test the protective efficacy of verapamil a calcium channel blocker (CCB) against gentamicin nephrotoxicity. They were divided into four groups: group-1 control, giving only water, group- 2, they were administered gentamicin IP 80 mg/Kg, group-3, administered verapamil orally 10 mg/Kg and group-4 administered gentamicin and verapamil at the same time. The period of study was 10 days. Nephrotoxicity was induced by using gentamicin for 7 days; this was evidenced by marked elevation in blood urea nitrogen (BUN) andplasma creatinine (Pcr) when compared with control. BUN increased from 20±1.4 to 205±7 and Pcr from 0.75±0.08 to 4.2±0.3 mg/100 ml). Coadministration of verapamil with gentamicin decreased the rise in BUN and Pcr, their values reached to 90±13 and 1.5±0.9 mg/100 ml respectively. Our data suggest that supplementation of verapamil decreased the severity of acute renal failure (ARF) in rats, this supports previous studies in which CCBs offer renoprotection in ARF, the exact mechanism of this protection need further study

DeepFMRI: And End-to-End Deep Network for Classification of FRMI Data

With recent advancements in machine learning, the research community has made tremendous advances towards the classification of neurological disorders from time-series functional MRI signals. However, existing classification techniques rely on hand-crafted features and classical machine learning models. In this paper, we propose an end-to-end model that utilizes the representation learning capability of deep learning to classify a neurological disorder from fMRI data. The proposed DeepFMRI model is comprised of three networks, namely (1) a feature extractor, (2) a similarity network, and (3) a classification network. The model takes fMRI raw time-series signals as input and outputs the predicted labels; and is trained end-to-end using back-propagation. Experimental results on the publicly available ADHD-200 dataset demonstrate that this innovative model outperforms previous state-of-the-art

Thermo-mechanical properties prediction of Ni-reinforced Al2_2O3_3 composites using micro-mechanics based representative volume elements

For effective cutting tool inserts that absorb thermal shock at varying temperature gradients, improved thermal conductivity and toughness are required. In addition, parameters such as the coefficient of thermal expansion must be kept within a reasonable range. This work presents a novel material design framework based on a multi-scale modeling approach that proposes nickel (Ni)-reinforced alumina (Al$_2$O$_3$) composites to tailor the mechanical and thermal properties required for ceramic cutting tools by considering numerous composite parameters. The representative volume elements (RVEs) are generated using the DREAM.3D software program and the output is imported into a commercial finite element software ABAQUS. The RVEs which contain multiple Ni particles with varying porosity and volume fractions are used to predict the effective thermal and mechanical properties using the computational homogenization methods under appropriate boundary conditions (BCs). The RVE framework is validated by the sintering of Al$_2$O$_3$-Ni composites in various compositions. The predicted numerical results agree well with the measured thermal and structural properties. The properties predicted by the numerical model are comparable with those obtained using the rules of mixtures and SwiftComp, as well as the Fast Fourier Transform (FFT) based computational homogenization method. The results show that the ABAQUS, SwiftComp and FFT results are fairly close to each other. The effects of porosity and Ni volume fraction on the mechanical and thermal properties are also investigated. It is observed that the mechanical properties and thermal conductivities decrease with the porosity, while the thermal expansion remains unaffected. The proposed integrated modeling and empirical approach could facilitate the development of unique Al$_2$O$_3$-metal composites with the desired thermal and mechanical properties for ceramic cutting inserts