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The role of input imaging combination and ADC threshold on segmentation of acute ischemic stroke lesion using U-Net

European Radiology 2023年第9期33卷 6157-6167页

作者： Li, Ya-Hui Lin, Shao-Chieh Chung, Hsiao-Wen Chang, Chia-Ching Peng, Hsu-Hsia Huang, Teng-Yi Shen, Wu-Chung Tsai, Chon-Haw Lo, Yu-Chien Lee, Tung-Yang Juan, Cheng-Hsuan Juan, Cheng-En Chang, Hing-Chiu Liu, Yi-Jui Juan, Chun-Jung Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Department of Medical Imaging China Medical University Hsinchu Hospital No. 199 Sec. 1 Xinglong Rd. Zhubei City Hsinchu County 302 Hsinchu Taiwan Ph.D. Program in Electrical and Communication Engineering Feng Chia University Taichung Taiwan Department of Electrical Engineering National Taiwan University Taipei Taiwan Department of Management Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Department of Electrical Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Department of Medical Imaging Medical University Hospital Taichung Taiwan Department of Neurology China Medical University Hospital Taichung Taiwan Cheng Ching Hospital Taichung Taiwan Master’s Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Automatic Control Engineering Feng Chia University No. 100 Wenhwa Rd. Seatwen Taichung 40724 Taiwan Department of Biomedical Engineering The Chinese University of Hong Kong ERB1112 11/F William M.W. Mong Engineering Building N.T Shatin Hong Kong Multi-Scale Medical Robotics Center The Chinese University of Hong Kong N.T Shatin Hong Kong Department of Biomedical Engineering National Defense Medical Center Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan

Background: To evaluate the effect of the weighting of input imaging combo and ADC threshold on the performance of the U-Net and to find an optimized input imaging combo and ADC threshold in segmenting acute ischemic stroke (AIS) lesion. Methods: This study retrospectively enrolled a total of 212 patients having AIS. Four combos, including ADC-ADC-ADC (AAA), DWI-ADC-ADC (DAA), DWI-DWI-ADC (DDA), and DWI-DWI-DWI (DDD), were used as input images, respectively. Three ADC thresholds including 0.6, 0.8 and 1.8 × 10^–3 mm²/s were applied. Dice similarity coefficient (DSC) was used to evaluate the segmentation performance of U-Nets. Nonparametric Kruskal–Wallis test with Tukey–Kramer post-hoc tests were used for comparison. A p <.05 was considered statistically significant. Results: The DSC significantly varied among different combos of images and different ADC thresholds. Hybrid U-Nets outperformed uniform U-Nets at ADC thresholds of 0.6 × 10^–3 mm²/s and 0.8 × 10^–3 mm²/s (p <.001). The U-Net with imaging combo of DDD had segmentation performance similar to hybrid U-Nets at an ADC threshold of 1.8 × 10^–3 mm²/s (p =.062 to 1). The U-Net using the imaging combo of DAA at the ADC threshold of 0.6 × 10^–3 mm²/s achieved the highest DSC in the segmentation of AIS lesion. Conclusions: The segmentation performance of U-Net for AIS varies among the input imaging combos and ADC thresholds. The U-Net is optimized by choosing the imaging combo of DAA at an ADC threshold of 0.6 × 10^–3 mm²/s in segmentating AIS lesion with highest DSC. Key Points: • Segmentation performance of U-Net for AIS differs among input imaging combos. • Segmentation performance of U-Net for AIS differs among ADC thresholds. • U-Net is optimized using DAA with ADC = 0.6 × 10^–3mm²/s. © 2023, The Author(s), under exclusive licence to European Society of Radiology.

关键词： Deep Learning Diffusion Magnetic Resonance Imaging Ischemic Stroke Neural Networks, Computer Retrospective Study

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Low-to-Moderate Arsenic Exposure and Urothelial Tract Cancers with a Long Latent Period of Follow-Up in an Arseniasis Area

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Journal of Epidemiology and Global Health 2023年第4期13卷 807-815页

作者： Liao, Pei-Ju Lee, Chih-Hung Wang, Shu-Li Chiou, Hung-Yi Chen, Chien-Jen Seak, Chen-June Wu, I-Wen Hsu, Kuang-Hung International Program of Health Informatics and Management Chang Gung University Taoyuan Taiwan Master Degree Program in Health and Long-Term Care Industry Chang Gung University Taoyuan Taiwan Division of Nephrology Chang Gung Memorial Hospital Keelung Taiwan Department of Dermatology Kaohsiung Chang Gung Memorial Hospital Kaohsiung Taiwan National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan Institute of Population Health Sciences National Health Research Institutes Zhunan Miaoli County Taiwan Master Program in Applied Epidemiology College of Public Health Taipei Medical University Taipei Taiwan Genomics Research Center Academia Sinica Taipei Taiwan Department of Emergency Medicine New Taipei Municipal Tucheng Hospital New Taipei City Taiwan Department of Emergency Medicine Lin-Kou Medical Center Chang Gung Memorial Hospital Taoyuan Taiwan College of Medicine Chang Gung University Taoyuan Taiwan Laboratory for Epidemiology Department of Health Care Management and Healthy Aging Research Center Chang Gung University No. 259 Wen-Hwa 1St Road Kwei-Shan Taoyuan 333 Taiwan Research Center for Food and Cosmetic Safety College of Human Ecology Chang Gung University of Science and Technology Taoyuan Taiwan Department of Safety Health and Environmental Engineering Ming Chi University of Technology New Taipei City Taiwan

Background: Arsenic exposure can cause adverse health effects. The effects of long-term low-to-moderate exposure and methylations remain unclear. Objective: This study aims to examine the association between low-to-moderate arsenic exposure and urothelial tract cancers while considering the effects of methylation capacity. Methods: In this study, 5,811 participants were recruited from an arseniasis area in Taiwan for inorganic arsenic metabolite analysis. This follow-up study was conducted between August 1995 and December 2017. We identified 85 urothelial tract cancers in these participants, including 49 bladder and 36 upper urothelial tract cancer cases. A Cox proportional hazards model was employed. Results: The analyses revealed a significant association between concentrations of inorganic arsenic in water > 100 ug/L and bladder cancer occurrence, with a hazard ratio (HR) of 4.88 (95% CI 1.35–17.61). A monotonic trend was observed between concentrations of inorganic arsenic in water (from 0 to > 100 ug/L) and the incidence of urothelial tract cancer, including bladder cancer (p < 0.05) and upper urothelial tract cancers (p < 0.05). Participants with a lower primary methylation index or higher secondary methylation index had a prominent effect. Conclusions: Rigorous regulations and active interventions should be considered for populations with susceptible characteristics. © 2023, The Author(s).

关键词： Bladder cancer Dose–response relationship Long latency arsenic exposure Methylation capacity Upper urothelial tract cancers

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Convolutional neural network-based fast seizure detection from video electroencephalograms

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Biomedical Signal Processing and Control 2023年第Part2期80卷 000-000页

作者： Chou, Chi-Hsiang Shen, Tsu-Wang Tung, Hsin Hsieh, Peiyuan F. Kuo, Chih-En Chen, Ting-Mao Yang, Chao-Wei Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan The Department of Neurological Institute Taichung Veterans General Hospital Taichung Taiwan Department of Neurology National Yang Ming Chiao Tung University School of Medicine Taipei Taiwan Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Master's Program in Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Department of Applied Mathematics National Chung Hsing University Taichung Taiwan

Objective: Ictal stage detection in electroencephalography (EEG) is important for epilepsy diagnosis. However, it's a laborious and time-consuming task for neurologists, especially with lengthy EEG recordings (such as video-EEG). Here we applied the current deep learning techniques to improve ictal stage detection efficiency. Methods: We enrolled epileptic patients at video-EEG examination beds of our neurology department between Jan 2016 and Apr 2020. Four stages of EEG (interictal, preictal, ictal and postictal) were labeled and confirmed by neurologists. Labeled EEG signals were transformed into second-order Poincaré difference plots. The whole dataset was divided into ‘Training’, ‘Validation’ and ‘Testing’ datasets with the proportions of 45%, 5% and 50% respectively. We applied 4 Convolutional Neural Networks (CNNs), namely, AlexNet, GoogLeNet, ResNet50 and DenseNet201, to learn classifying. The top three CNNs were put into an ensemble model for datasets classification. Results: 30 epileptic patients were included: Male/Female 16/14, mean age 42.8 ± 15.0 (range 22 ∼ 81) years old. Total recorded ictal duration was 47,444 s. Overall accuracies for each of the 4 CNN models (AlexNet, GoogLeNet, ResNet50 and DenseNet201) were 78.0%, 78.3%, 81.7% and 80.8%. We omitted the AlexNet, the other 3 CNNs were put into the ensemble model. The best performance was under test with 1% of the Testing dataset yielded the overall accuracies of 85.9%. The ictal stage accuracy was 97.7%. Conclusion: Our method is a promising computer-aid deep learning approach to detect seizures on video EEG. Significance: For testing phase, selected proportion of testing dataset may influence the performances of deep learning models. © 2022 The Author(s)

关键词： Electroencephalography

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An aerodynamic analysis of energy saving car based on computational fluid dynamic using solidworks

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AIP Conference Proceedings 2024年第1期3222卷

作者： R. F. Naryanto A. Setiyawan A. Roziqin S. Maulana F. B. Darsono I. G. M. Andika M. K. Delimayanti R. A. Widodo Mechanical Engineering Department Universitas Negeri Semarang Semarang Indonesia Automotive Engineering Education Universitas Negeri Semarang Semarang Indonesia Computer and Informatics Engineering Department Politeknik Negeri Jakarta Depok Indonesia Master Program in Applied Electrical Engineering Politeknik Negeri Jakarta Depok Indonesia

Improved transportation increased the amount of fuel consumption required. Transportation in Indonesia is the largest oil-consuming sector, with a percentage of 40.1% of the total. Vehicle manufacturers are required to produce a vehicle that has a high level of fuel efficiency. This study aims to determine the aerodynamics of energy-efficient vehicle bodies based on computational fluid dynamics (CFD). This research was conducted using SolidWorks to implement the model. This study examined the variations in airflow velocity at 25, 40, 55, and 70 km/hour. It can be found that the coefficient of drag (Cd) values of the latest body designs with variations in airflow velocity were increased value. The calculation results of the coefficient of drag values were 0.149, 0.154, 0.162, and 0.162, respectively, and the average value was 0.156. In this research, we have found that the energy-saving car body can reduce the drag coefficient compared with the previous study.

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Hybrid ACO and PSO to minimize makespan in job shop scheduling problems

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AIP Conference Proceedings 2024年第1期3222卷

作者： Priljen Habeahan Erna Budhiarti Nababan Mahyuddin K. M. Nasution Informatics Engineering Master Program Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Data Science and Artificial Intelligence Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia

The lowest time search in the dataset that E. Taillard utilized employs a heuristic approach based on tabu search techniques to get the predicted solution. Glover’s study gives a broad description of tabu search, which is commonly encountered in Taillard’s job shop scheduling difficulties and Widmer et al.’s flow shop sorting challenges. Although tabu search is relatively simple to use and typically yields excellent results, it takes a long time to complete. In this research a hybrid ACO and PSO was carried out to minimize makespan in the Job Shop Scheduling Problem which was used as sourced from benchmark data which is secondary data obtained from E. Taillard “Benchmarks for basic scheduling problems” which consists of job shop matrix data (job × machine) measuring 4 × 4, 5 × 5, 7 × 7, 10 × 10, 15 × 15, 20 × 20, 30 × 15, 30 × 20, 50 × 15 and 50 × 20. Hybrid is carried out by calculating the Pbest value, namely the process position of each job on the machine to get the best solution using the PSO algorithm. Next, calculate the Gbest (Global best) value for the position of each job on the best machine on the entire machine using the PSO algorithm and initialize the ACO parameters using the PBest and Gbest values. The results of research on datasets with sizes 10×10, 15×15, 20×20, 30×15, 30×20, 50×15 and 50×20 produce smaller makespan compared to the lower bound on the dataset with an average minimum makespan improvement value of 1.184.

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A potential imaging biomarker distinguishing neurodegenerative parkinsonism using brainstem MRI shape analysis

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European radiology 2023年第7期33卷 4537-4539页

作者： Ming-Ting Tsai Cheng-En Juan Yi-Jui Liu Chun-Jung Juan Department of Medical Imaging China Medical University Hsinchu Hospital Hsinchu Taiwan Republic of China. Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Republic of China. Master's Program of Biomedical Informatics and Biomedical Engineering Feng Chia University Taichung Taiwan Republic of China. erliu@fcu.edu.tw. Department of Automatic Control Engineering Feng Chia University Taichung Taiwan Republic of China. erliu@fcu.edu.tw. Department of Medical Imaging China Medical University Hsinchu Hospital Hsinchu Taiwan Republic of China. peterjuancj@yahoo.com.tw. Department of Radiology School of Medicine College of Medicine China Medical University Taichung Taiwan Republic of China. peterjuancj@yahoo.com.tw. Department of Medical Imaging China Medical University Hospital Taichung Taiwan. peterjuancj@yahoo.com.tw. Department of Biomedical Engineering and Environmental Sciences National Tsing Hua University Hsinchu Taiwan Republic of China. peterjuancj@yahoo.com.tw. Department of Radiology School of Medicine National Defense Medical Center Taipei Taiwan Republic of China. peterjuancj@yahoo.com.tw.

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Detection Of CT - Scan Lungs COVID-19 Image Using Convolutional Neural Network And CLAHE

Detection Of CT - Scan Lungs COVID-19 Image Using Convolutio...

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International Conference on Information and Communications Technology (ICOIACT)

作者： Ronaldus Morgan James Andi Sunyoto Master Program of Informatics Engineering Universitas Amikom Yogyakarta Yogyakarta Indonesia Faculty of Computer Science Universitas Amikom Yogyakarta Yogyakarta Indonesia

ISBN: (数字)9781728173566

ISBN: (纸本)9781728173573

Detecting COVID-19 is a significant task for medical professionals today because of its rapid spread. To overcome this problem, medical professionals have used various techniques and methods to detect to inhibit the proliferation of COVID-19. CT (Computed Tomography) Scan is currently the best method for detecting COVID-19. This diagnostic method is very accurate because it can see organs in three dimensions. However, this method requires a radiologist to detect the disease and requires a long time, which means it will cut valuable time for medical practitioners if a patient is sick. Therefore it is necessary to implement a system to detect the coronavirus automatically as an alternative quickly. This study intends to help medical practitioners to detect computed tomography (CT) Scans of lungs infected with COVID-19. The methods to be used are Limited Adaptive histogram equalization (CLAHE) contrast to improve the quality of CT (Computed Tomography) Scan images of COVID-19 lungs and Convolutional Neural Network (CNN) for the image classification process. The dataset used is 698 RGB images. This study uses three convolutional layers, 3 Max-Pooling layers, and two fully connected layers, resulting in 83.28% accuracy.

关键词： COVID-19 Histograms Computed tomography Neural networks Lung Convolutional neural networks Medical diagnostic imaging

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Service Oriented Design for Indonesian E-Government System Using SOA 6

Service Oriented Design for Indonesian E-Government System U...

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Joint Conference of the 6th Annual Conference of Industrial and System engineering 2019, ACISE 2019 and 1st International Conference on Risk Management as an Interdisciplinary Approach 2019, ICRMIA 2019

作者： Fajar, A.N. Shofi, I.M. Information Systems Management Department BINUS Graduate Program-Master of Information Systems Management Bina Nusantara University Jakarta11480 Indonesia Informatics Engineering UIN Syarif Hidayatullah Indonesia

The objective of this study is to create a services design of E-Government system that can help Indonesian government to resolve some problems such as diversity platform and application. It causes the island applications that can make decision support processes is not well happen. In this study, we use literature review as an approach to discuss the phenomenon. It is performed by identifying, evaluating, and combining information from several other studies which relevant with this study's research questions. We use services oriented analysis and design approach to discussing the phenomenon. The ability of Service-oriented Architecture to connect multiple services between diversity systems and it can help the enterprise application travelling in Indonesian government. The case in this study is travelling system in Indonesia Government. The results in this study are the conceptual services for Travelling System using SOA approach and ESB Technology. It consists of several services and business services which is can be integrated among of them. © Published under licence by IOP Publishing Ltd.

关键词： Information services

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Applying mobility business intelligence concept in analyzing oil palm plantation productivity

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AIP Conference Proceedings 2024年第1期3026卷

作者： Andreas Wahyu Krisdiarto Irya Wisnubhadra Anzaludin Samsinga Perbangsa Teddy Suparyanto Bens Pardamean Faculty of Agricultural Technology STIPER Institute of Agriculture Yogyakarta Indonesia Informatics Engineering Department Atma Jaya Yogyakarta University Yogyakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia BDSRC Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

The palm oil business employs almost 20 million people, generates USD 21 billion in revenue, and plays a vital role in Indonesia’s social economy. The Fresh Fruit Bunches (FFB) to Palm Oil Mills (POM) distribution system is one important aspect of fruit quality. Three steps are involved in getting Oil Palm FFB from the plantation to the POM. The first part of the procedure involves cutting FFB from the tree, the second stage involves gathering the fruit at a fruit collection point (FCP), and the third stage involves transporting the fruit to the palm oil mill (POM). As of now, the cost of the FFB transportation is still considerable, accounting for roughly 15% to 20% of the FFB pricing. The use of the Business Intelligence (BI) idea in the oil palm harvesting system is presented in this study as a foundation for creating web-based applications.

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Sentiment Analysis using Recurrent Neural Network

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Journal of Physics: Conference Series 2020年第1期1471卷

作者： Lilis Kurniasari Arif Setyanto Master's Program of Informatics Engineering Universitas AMIKOM Yogyakarta Indonesia

This study aims to measure the accuracy of the sentiment analysis classification model using deep learning and neural networks. This study used the algorithm Recurrent Neural Network (RNN) and Word2vec. No previous research has used this model to analyze sentiments written using Indonesian language so that the level of accuracy is unknown. The research began by making a classification model of sentiment analysis. Then, the model was tested through experiments. In this study, They used two classifications (positive and negative). Experiments are carried out using training data sets and the test used data sets sourced from Traveloka theybsite. The result shows that the model presents outstanding results and reaches about 91.9%.

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