Traffic violations analysis: Identifying risky areas and common violations.

Road traffic accidents caused by traffic violations are a major public health issue that results in loss of lives and economic costs. Therefore, it is important to prioritize road safety measures that reduce the incidence and severity of accidents. In this study, we suggest an incremental road safety strategy that identifies high-risk areas and common traffic violations in order to prioritize further enforcement. In fact, by analyzing data on traffic violations in different districts and comparing them to the overall average using the Kolmogorov-Smirnov (KS) test, risky areas are identified and the most common violations are detected. We performed a comparison between several types of clustering optimizations to spot clusters to be enforced in order to reduce violations. Our results indicate that some Districts have a higher risk of traffic violations than others do, and some violations (Speeding, Registration, License, Belt, Influence, Phone, etc.) are more common than others are. We also find that k-means clustering provides the best results for identifying clusters of violations records and optimizing enforcement strategies. Our findings can be adopted by law enforcement agencies to focus on high-risk areas and target the most common violations in order to optimize their resources and improve road safety.

Towards an Accurate MRI Acute Ischemic Stroke Lesion Segmentation Based on Bioheat Equation and U-Net Model.

Acute ischemic stroke represents a cerebrovascular disease, for which it is practical, albeit challenging to segment and differentiate infarct core from salvageable penumbra brain tissue. Ischemic stroke causes the variation of cerebral blood flow and heat generation due to metabolism. Therefore, the temperature is modified in the ischemic stroke region. In this paper, we incorporate acute ischemic stroke temperature profile to reinforce segmentation accuracy in MRI. Pennes bioheat equation was used to generate brain thermal images that may provide rich information regarding the temperature change in acute ischemic stroke lesions. The thermal images were generated by calculating the temperature of the brain with acute ischemic stroke. Then, U-Net was used in this paper for the segmentation of acute ischemic stroke. A dataset of 3192 images was created to train U-Net using k-fold crossvalidation. The training time was about 10 hours and 35 minutes in NVIDIA GPU. Next, the obtained trained model was compared with recent methods to analyze the effect of the ischemic stroke temperature profile in segmentation. The obtained results show that significant parts of acute ischemic stroke and background areas are segmented only in thermal images, which proves the importance of using thermal information to improve the segmentation outcomes in MRI diagnosis.

Automatic cardiac cine MRI segmentation and heart disease classification.

Cardiac cine magnetic resonance imaging (MRI) continues to be recognized as an established modality for non-invasive assessment of the function and structure of the cardiovascular system. Making full use of fully convolutional neural networks CNNs ability to operate pixel-wise classification, cine MRI sequences can be segmented and volumetric features of three key heart structures are computed for disease prediction. The three key heart structures are the left ventricle cavity, right ventricle cavity and the left ventricle myocardium. In this paper, we suggest an automated pipeline for both cardiac segmentation and diagnosis. The study was conducted on a dataset of 150 patients from Dijon Hospital in the context of the post-2017 Medical Image Computing and Computer Assisted Intervention MICCAI, Automated Cardiac Diagnosis Challenge (ACDC). The challenge consists in two phases: (i) a segmentation contest, where performance is evaluated on dice overlap coefficient and Hausdorff distance metrics, and a (ii) diagnosis contest for heart disease classification. For this aim, we propose the use of a deep learning based network for segmentation of the three key cardiac structures within short-axis cine MRI sequences and a classifier ensemble for heart disease classification. The deep learning segmentation network is a UNet fully convolutional neural network variant with fewer trainable parameters. The classifier ensemble consists in combining three classifiers, namely a multilayer perceptron, a random forest and a support vector machine. Before feeding the segmentation network, a preliminary step consists in localizing heart region and cropping input images to a restricted region of interest (ROI). This is achieved by a signal processing based approach and aims at reducing multi-class imbalance and computational load. We achieved nearly state of the art accuracy performances for both the segmentation and disease classification challenges. Reporting a mean dice overlap coefficient of 0.92 for the three cardiac structures segmentation, along with good limits of agreement for the various derived clinical indices, leading to an accuracy of 0.92 for the disease classification on unseen data.

Towards Reinforced Brain Tumor Segmentation on MRI Images Based on Temperature Changes on Pathologic Area.

Brain tumor segmentation is the process of separating the tumor from normal brain tissues; in clinical routine, it provides useful information for diagnosis and treatment planning. However, it is still a challenging task due to the irregular form and confusing boundaries of tumors. Tumor cells thermally represent a heat source; their temperature is high compared to normal brain cells. The main aim of the present paper is to demonstrate that thermal information of brain tumors can be used to reduce false positive and false negative results of segmentation performed in MRI images. Pennes bioheat equation was solved numerically using the finite difference method to simulate the temperature distribution in the brain; Gaussian noises of ±2% were added to the simulated temperatures. Canny edge detector was used to detect tumor contours from the calculated thermal map, as the calculated temperature showed a large gradient in tumor contours. The proposed method is compared to Chan-Vese based level set segmentation method applied to T1 contrast-enhanced and Flair MRI images of brains containing tumors with ground truth. The method is tested in four different phantom patients by considering different tumor volumes and locations and 50 synthetic patients taken from BRATS 2012 and BRATS 2013. The obtained results in all patients showed significant improvement using the proposed method compared to segmentation by level set method with an average of 0.8% of the tumor area and 2.48% of healthy tissue was differentiated using thermal images only. We conclude that tumor contours delineation based on tumor temperature changes can be exploited to reinforce and enhance segmentation algorithms in MRI diagnostic.

3D brain tumor localization and parameter estimation using thermographic approach on GPU.

The aim of this paper is to present a GPU parallel algorithm for brain tumor detection to estimate its size and location from surface temperature distribution obtained by thermography. The normal brain tissue is modeled as a rectangular cube including spherical tumor. The temperature distribution is calculated using forward three dimensional Pennes bioheat transfer equation, it's solved using massively parallel Finite Difference Method (FDM) and implemented on Graphics Processing Unit (GPU). Genetic Algorithm (GA) was used to solve the inverse problem and estimate the tumor size and location by minimizing an objective function involving measured temperature on the surface to those obtained by numerical simulation. The parallel implementation of Finite Difference Method reduces significantly the time of bioheat transfer and greatly accelerates the inverse identification of brain tumor thermophysical and geometrical properties. Experimental results show significant gains in the computational speed on GPU and achieve a speedup of around 41 compared to the CPU. The analysis performance of the estimation based on tumor size inside brain tissue also presented.