Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis

In this work, we present a comparison of a shallow and a deep learning architecture for the automated segmentation of white matter lesions in MR images of multiple sclerosis patients. In particular, we train and test both methods on early stage disease patients, to verify their performance in challenging conditions, more similar to a clinical setting than what is typically provided in multiple sclerosis segmentation challenges. Furthermore, we evaluate a prototype naive combination of the two methods, which refines the final segmentation. All methods were trained on 32 patients, and the evaluation was performed on a pure test set of 73 cases. Results show low lesion-wise false positives (30%) for the deep learning architecture, whereas the shallow architecture yields the best Dice coefficient (63%) and volume difference (19%). Combining both shallow and deep architectures further improves the lesion-wise metrics (69% and 26% lesion-wise true and false positive rate, respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho

The influence of quantization process on the performance of global entropic thresholding algorithms using electrical capacitance tomography data

In measuring component fraction in multiphase flows using tomographic techniques, it is desirable to use a high speed tomography system capable of generating 100 tomograms per second. The electrical capacitance tomography system in this regard is considered to be the best among the available tomographic techniques. However, due to its inherent limitations the system generates distorted reconstructed tomograms necessitating the use of extra signal processing techniques such as thresholding to minimize these distortions. Whilst thresholding technique has been effective in minimizing distortions, the additional computation associated with the process limits the speed of tomogram generation desired from the system. Further, the accuracy of the techniques is limited to higher ranges of the full component fraction range. However, since its performance can be influenced by the nature of the quantization process required a priori, optimal quantization parameters can be found and used to improve performance. In this article the influence of quantization resolution and its rate on the performance of global entropic thresholding algorithms have been investigated. Measurement of gas volume component fraction in a multiphase flow of gas/liquid mixture using electrical capacitance tomography system has been used for evaluation using simulated and online capacitance measurement data. Results show that an optimal quantizer resolution is flow regime dependent. Higher resolutions are optimal for annular flow and vice versa for stratified flow regimes. Also, higher resolution significantly minimizes the dependency of the thresholding algorithm on the object to be searched, thereby reducing complexity of designing a thresholder. Overall, the optimal quantization resolution is 256. Tanzania Journal of Science Vol. 31 (2) 2005: pp. 63-7

Evaluation of Adaptive Signal Control Technology—Volume 2: Comparison of Base Condition to the First Year After Implementation

Field evaluation of adaptive signal control technologies (ASCT) is very important in understanding the system’s contribution to safety and operational efficiency. Data were collected at six intersections along the Neil Street corridor in Champaign, Illinois, before deployment of SynchroGreen, an ASCT system. The volume, delay, and queue length data from the field for the “before” conditions (which is 2013 data) were compared to the data from the first year after implementation conditions (which is 2015 data). The system was installed in early 2015 and fined tuned by the vendor to get the “best” performance. The field volumes were compared for 83 lane groups (approaches). While traffic volume on 48% of the lane groups significantly increased, 48% did not change significantly, and only 4% significantly decreased. The field delays were compared for 83 lane groups; out of which 22% showed significant increase, 64% showed no significant change, and 14% showed significant decrease. Queue length was compared for only 63 lane groups because the remaining 20 lane groups either did not have queue data, or the queue length was insignificant (two cars or less). Out of the 63 lane groups, 32% showed significant increase, but 49% showed no significant change, and 19% showed significant decrease in queue length. ASCT performance was evaluated based on the changes in volume, delay, and queue length combined. An overall performance indicator (PI) was determined as: Imp (Improved), Unch (Unchanged), Det (Deteriorated), or Mix (mixed results). Of the 83 lane groups analyzed, 51% showed improvement, 20% remained unchanged, 28% showed deterioration, and 1% showed a mixed result. The analyses indicated that ASCT made a compromise between the minor and major street performances and, in general, the minor street improvements were correlated with the major street deterioration or unchanged performances.IDOT-R27-127Ope

WaterMet2: a tool for integrated analysis of sustainability-based performance of urban water systems

This paper presents the "WaterMet2" model for long-term assessment of urban water system (UWS) performance which will be used for strategic planning of the integrated UWS. WaterMet2 quantifies the principal water-related flows and other metabolism-based fluxes in the UWS such as materials, chemicals, energy and greenhouse gas emissions. The suggested model is demonstrated through sustainability-based assessment of an integrated real-life UWS for a daily time-step over a 30-year planning horizon. The integrated UWS modelled by WaterMet2 includes both water supply and wastewater systems. Given a rapid population growth, WaterMet2 calculates six quantitative sustainability-based indicators of the UWS. The result of the water supply reliability (94%) shows the need for appropriate intervention options over the planning horizon. Five intervention strategies are analysed in WaterMet2 and their quantified performance is compared with respect to the criteria. Multi-criteria decision analysis is then used to rank the intervention strategies based on different weights from the involved stakeholders' perspectives. The results demonstrate that the best and robust strategies are those which improve the performance of both water supply and wastewater systems

Computational depth of anesthesia via multiple vital signs based on artificial neural networks

This study evaluated the depth of anesthesia (DoA) index using artificial neural networks (ANN) which is performed as the modeling technique. Totally 63-patient data is addressed, for both modeling and testing of 17 and 46 patients, respectively. The empirical mode decomposition (EMD) is utilized to purify between the electroencephalography (EEG) signal and the noise. The filtered EEG signal is subsequently extracted to achieve a sample entropy index by every 5-second signal. Then, it is combined with other mean values of vital signs, that is, electromyography (EMG), heart rate (HR), pulse, systolic blood pressure (SBP), diastolic blood pressure (DBP), and signal quality index (SQI) to evaluate the DoA index as the input. The 5 doctor scores are averaged to obtain an output index. The mean absolute error (MAE) is utilized as the performance evaluation. 10-fold cross-validation is performed in order to generalize the model. The ANN model is compared with the bispectral index (BIS). The results show that the ANN is able to produce lower MAE than BIS. For the correlation coefficient, ANN also has higher value than BIS tested on the 46-patient testing data. Sensitivity analysis and cross-validation method are applied in advance. The results state that EMG has the most effecting parameter, significantly.This research is financially supported by the Ministry of Science and Technology (MOST) of Taiwan. This research is also supported by the Centre for Dynamical Biomarkers and Translational Medicine, National Central University, Taiwan, which is also sponsored by MOST (MOST103-2911-I-008-001). Also, it is supported by National Chung-Shan Institute of Science & Technology in Taiwan (Grant nos. CSIST-095-V301 and CSIST-095-V302)

Worm algorithms for the 3-state Potts model with magnetic field and chemical potential

We discuss worm algorithms for the 3-state Potts model with external field and chemical potential. The complex phase problem of this system can be overcome by using a flux representation where the new degrees of freedom are dimer and monomer variables. Working with this representation we discuss two different generalizations of the conventional Prokof'ev-Svistunov algorithm suitable for Monte Carlo simulations of the model at arbitrary chemical potential and evaluate their performance.Comment: 23 pages, 8 figure