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Slicer-DeepSeg: Open-Source Deep Learning Toolkit for Brain Tumour Segmentation

Current Directions in Biomedical engineering 2021年第1期7卷

作者： Zeineldin, Ramy A. Weimann, Pauline Karar, Mohamed E. Mathis-Ullrich, Franziska Burgert, Oliver Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany Research Group Computer Assisted Medicine Reutlingen University Reutlingen Germany Faculty of Electronic Engineering (FEE) Menoufia University Menouf Egypt

Purpose computerized medical imaging processing assists neurosurgeons to localize tumours precisely. It plays a key role in recent image-guided neurosurgery. Hence, we developed a new open-source toolkit, namely Slicer-DeepSeg, for efficient and automatic brain tumour segmentation based on deep learning methodologies for aiding clinical brain research. Methods Our developed toolkit consists of three main components. First, Slicer-DeepSeg extends the 3D Slicer application and thus provides support for multiple data input/output data formats and 3D visualization libraries. Second, Slicer core modules offer powerful image processing and analysis utilities. Third, the Slicer-DeepSeg extension provides a customized GUI for brain tumour segmentation using deep learning-based methods. Results The developed Slicer-DeepSeg was validated using a public dataset of high-grade glioma patients. The results showed that our proposed platform's performance considerably outperforms other 3D Slicer cloud-based approaches. Conclusions Developed Slicer-DeepSeg allows the development of novel AIassisted medical applications in neurosurgery. Moreover, it can enhance the outcomes of computer-aided diagnosis of brain tumours. Open-source Slicer-DeepSeg is available at ***/razeineldin/Slicer-DeepSeg. © 2021 by Walter de Gruyter Berlin/Boston.

关键词： Magnetic resonance imaging

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Construction of the Shewhart Median Chart Based on the Expected Percentile Run Length

Construction of the Shewhart Median Chart Based on the Expec...

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IEEE International Conference on Control System, Computing and engineering (ICCSCE)

作者： Z. L. Chong W. L. Teoh H. W. You W. C. Yeong M. B. C. Khoo Department of Electronic Engineering Faculty of Engineering and Green Technology Universiti Tunku Abdul Rahman Kampar Malaysia School of Mathematical and Computer Sciences Heriot-Watt University Malaysia Putrajaya Malaysia Pusat GENIUS@Pintar Negara Universiti Kebangsaan Malaysia UKM Bangi Selangor Malaysia School of Mathematical Sciences Sunway University Petaling Jaya Malaysia School of Mathematical Sciences Universiti Sains Malaysia Penang Malaysia

A recent study explored the construction of the Shewhart median (S-M) chart by utilizing the percentile-based (PL) method. By using the PL method, practitioners can ascertain that the required conditions on run length (RL) performances for both in-control (IC) and out-of-control (OC) cases are met with desired probabilities. They conducted a comprehensive investigation on the IC and OC capabilities of the S-M chart utilizing the average run length (ARL), median run length (MRL), and PL methods for known shift sizes, and concluded that by adopting the PL method, the S-M chart demonstrated a noteworthy enhancement in the IC performance in comparison to the ARL method without causing much deterioration to the OC performance when $\delta\geq 1.5$. However, in various practical scenarios, the exact shift size is commonly unknown. Hence, we propose a new performance measure called the expected percentile run length (EPRL), where 50 th EPRL is equivalent to expected MRL (EMRL). It can be inferred that by adopting the PL method, the S-M chart demonstrated a noteworthy enhancement in the IC performance in comparison to the ARL method without causing much deterioration to the OC performance when $(\delta_{\mathrm{m}\ln},\delta_{\max})=(1.5,2.0)$ for unknown shift sizes. We illustrate the application of the proposed method using a real-life example based on the hard-baking process.

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Regional agricultural land classification based on random forest (Rf), decision tree, and svms techniques 4th

Regional agricultural land classification based on random fo...

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4th International Congress on Information and Communication technology, ICICT 2019

作者： Azeez, Nassr Yahya, Wafa Al-Taie, Inas Basbrain, Arwa Clark, Adrian School of Computer Science and Electronic Engineering University of Essex Colchester United Kingdom Faculty of Science Computer Department University of Baghdad Baghdad Iraq Faculty of Computing and Information Technology University of King Abdul-Aziz Jeddah Saudi Arabia Faculty of Education Mathematics Department University of Al-Hamdaniya Ninawa Iraq

ISBN: (纸本)9789811506369

Land cover observation based on remote sensing data demands robust classification techniques which give the precise complex land cover mapping. Scientists and researchers made great efforts in improving classification accuracy considerably. The aim of this paper is to show outcomes gained from the RF classifier and decision tree and to compare their effectiveness with the SVMs technique. The mentioned techniques are applied over the imagery we have captured with six different classes of ROI (Region Of Interest) images including unknown range. Results indicated that the performance of the random forest classifier outperforms the decision tree and SVMs techniques performance in terms of the number of mis-classifications instances and the classification accuracy with an overall accuracy of 86%, while the decision tree accuracy is 67%, and the SVMs accuracy is 56%, respectively. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Decision trees

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Exploring Issues of Story-Based Effort Estimation in Agile Software Development

SSRN

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SSRN 2023年

作者： Iqbal, Muhammad Ijaz, Muhammad Mazhar, Tehseen Shahzad, Tariq Abbas, Qamar Ghadi, Yazeed Yasin Ahmed, Waseem Hamam, Habib Institute of Computing and Information Technology Gomal University Dera Ismail Khan KP Pakistan Department of Computer Science Virtual University of Pakistan Lahore55150 Pakistan Department of Computer Sciences COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan Department of Computer Science FCIT International Islamic University Islamabad Pakistan Department of computer science and software engineering Al Ain University Abu Dhabi15322 United Arab Emirates Department of Computer Science and Information Technology University of Malakand Pakistan Faculty of Engineering University of de Moncton MonctonNBE1A3E9 Canada Commune d'Akanda B.P. Libreville1989 Gabon School of Electrical Engineering Department of Electrical and Electronic Engineering. Science University of Johannesburg Johannesburg2006 South Africa Spectrum of Knowledge Production & Skills Development Sfax3027 Tunisia

Objctives This research delves into the domain of user story-based effort estimation in agile software development, aiming to explore the issues arising from inaccurate estimations. The primary goal is to uncover these issues comprehensively and propose potential solutions, thus enhancing the efficacy. Methods To achieve the research objectives, a systematic literature review (SLR) is conducted, surveying a wide range of sources to gather insights into issues surrounding user story-based effort estimation. Results The SLR reveals a spectrum of issues undermining the accuracy of user story-based effort estimation. It identifies internal factors like communication, team expertise, and composition. © 2023, The Authors. All rights reserved.

关键词： Software design

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Efficient VM Placement Policy for Data Centre in Cloud Environment 3rd

Efficient VM Placement Policy for Data Centre in Cloud Envir...

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3rd International Conference on Soft Computing: Theories and Applications, SoCTA 2018

作者： Dubey, Kalka Nasr, Aida A. Sharma, S.C. El-Bahnasawy, Nirmeen Attiya, Gamal El-Sayed, Ayman Cloud Computing and Wireless sensor Lab Indian Institute of Technology Roorkee RoorkeeUttarakhand247664 India Faculty of Artificial Intelligence Kafrelsheikh University Kafr Elsheikh Egypt Computer Science and Engineering Department Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt

ISBN: (纸本)9789811507502

Cloud computing now becomes the accomplished infrastructure model for delivering the cloud service to its user over the Internet. Virtualization is one of the key concepts, which makes the dynamic sharing of physical resources of cloud more effective. In virtualization, virtual machine is placed over the physical machine in data centre and VM placement is one of the challenging issues in cloud computing, so cloud service provider needs good VM placement policy to increase the utilization of physical resources as well as reduce the load-balancing problem. In this paper, we proposed a new VM placement policy called Efficient of service Best Fit Decreasing (EBFD). From the simulation experiments result, we see that the proposed policy performs much better performance compared with first-fit and best-fit policy. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Cloud computing

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Humanitarian relief network design: Responsiveness maximization and a case study of Typhoon Rammasun

A I I E Transactions

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A I I E Transactions 2023年第3期55卷 301-313页

作者： Jia Shu Miao Song Beilun Wang Jing Yang Shaowen Zhu a School of Management and Economics University of Electronic Science and Technology of China Chengdu China b Department of Logistics and Maritime Studies Faculty of Business The Hong Kong Polytechnic University Hong Kong China c School of Computer Science and Engineering Southeast University Nanjing China d Department of Management Science and Engineering School of Economics and Management Southeast University Nanjing China d Department of Management Science and Engineering School of Economics and Management Southeast University Nanjing Chinae Modern Logistics Center National University of Singapore (Chongqing) Research Institute Chongqing China

In this article, we study a humanitarian relief network design problem, where the demand for relief supplies in each affected area is uncertain and can be met by more than one relief facility. Given a certain cost budget, we simultaneously optimize the decisions of relief facility location, inventory pre-positioning, and relief facility to affected area assignment so as to maximize the responsiveness. The problem is formulated as a chance-constrained stochastic programming model in which a joint chance constraint is utilized to measure the responsiveness of the humanitarian relief network. We approximate the proposed model by another model with chance constraints, which can be solved based on two settings of the demand information in each affected area: (i) the demand distribution is given; and (ii) the partial demand information, e.g., the mean, the variance, and the support, is given. We use a case study of the 2014 Typhoon Rammasun to illustrate the application of the model. Computational results demonstrate the effectiveness of the solution approaches and show that the chance-constrained stochastic programming models are superior to the deterministic model for humanitarian relief network design.

关键词： Responsiveness maximization humanitarian relief network design chance-constrained stochastic programming

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Deep Networks for Medical Images Classification, a Comparative Study

Deep Networks for Medical Images Classification, a Comparati...

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Artificial Intelligence & Green Energy (ICAIGE), IEEE International Conference on

作者： Hussain K. Ibrahim Nizar Rokbani Ali Wali Adel M. Alimi National school of electronics and telecommunications (ENET'COM) of Sfax University Sfax Tunisia High Institute of Applied Sciences Technology of Sousse University of Sousse Tunisia Research Groups in Intelligent Machines (REGIM Laboratory) National Engineering School of Sfax (ENIS)University of Sfax Tunisia Information Technology Wasit University Wasit Iraq Department of Biomedical Technology College of Applied Medical Sciences in Al-Kharj Prince Sattam bin Abdulaziz University Al-Kharj Saudi Arabi Higher Institute of Computer Science and Multimedia of Sfax University of Sfax Tunisia Research Groups in Intelligent Machines (REGIM Laboratory) National Engineering School of Sfax (ENIS) Sfax University Sfax Tunisia Department of Electrical and Electronic Engineering Science Faculty of Engineering and the Built Environment University of Johannesburg Johannesburg South Africa

ISBN: (数字)9798350389838

ISBN: (纸本)9798350389845

Machine learning (ML) and deep learning (DL) have had a significant impact on healthcare, particularly in the field of medical picture categorization, in recent years. These technologies provide a multitude of advantages that enhance the quality of patient care and increase the effectiveness of healthcare services. The primary rationale for employing (ML) and (DL) in medical picture categorization lies in their remarkable capacity to identify illnesses at an early stage. Automated classification systems has the ability to rapidly analyses and interpret pictures, surpassing the speed of human capabilities. This is particularly vital for effectively handling extensive datasets and becomes critical in emergency scenarios where prompt diagnosis is of utmost importance. The objective of this investigation is to categorize medical images into four distinct categories: normal, COVID-19, lung opacity, and viral pneumonia. In this paper three deep learning models were implemented: the classical CNN, AlexNet and GoogleNet. The comparative analysis showed that AlexNet achieved an accuracy of 96.74%, a processing time of 225 seconds, and a loss of 0.1192. The GoogleNet architecture recorded an accuracy of 93.98%, a processing time of 233 seconds, and a loss of 0.1945 while the classical CNN achieved an accuracy of 94.56%, a processing time of 315 seconds, and a loss of 0.1298. Results showed that the AlexNet has a little advantage in regrads to GoogleNet and classical CNN which report very close performances'.

关键词： Deep learning COVID-19 Green energy Accuracy Pneumonia Lungs Medical services Artificial neural networks Medical diagnostic imaging Image classification

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B-scan-sectioned dynamic micro-optical coherence tomography for bulk-motion suppression

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Chinese Optics Letters 2022年第2期20卷 34-39页

作者： Chaoliang Chen Weisong Shi Zhiyuan Qiu Victor X.D.Yang Wanrong Gao Advanced Photonics Center School of Electronic Science&EngineeringSoutheast UniversityNanjing 210096China Department of Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Electrical Computerand Biomedical EngineeringRyerson UniversityTorontoOntario M5B 2K3Canada Division of Neurosurgery Sunnybrook Health Sciences CentreTorontoOntario M4N 3M5Canada Division of Neurosurgery Faculty of MedicineUniversity of TorontoTorontoOntario M5T 1P5Canada

In this Letter, we present B-scan-sectioned dynamic micro-optical coherence tomography(BD-MOCT) for high-quality subcellular dynamic contrast imaging. Dynamic micro-optical coherence tomography(D-MOCT) is a functional optical coherence tomography(OCT) technique performed on high-resolution(micron level) OCT systems;hundreds of consecutive B-scans need to be acquired for dynamic signal extraction, which requires relatively long data acquisition time. Bulk motions occurring during data acquisition(even at the micron level) may degrade the quality of the obtained dynamic contrast images. In BD-MOCT, each full B-scan is divided into several sub-B-scans, and each sub-B-scan repeats multiple times before the sample beam moves to the next sub-B-scan. After all of the sub-B-scans for a full B-scan are completely acquired, we stitch all of the sub-B-scans into the same number of full B-scans. In this way, the time interval between two consecutive stitched B-scans could be reduced multiple times for bulk-motion suppression. The performed scanning protocol modulates the scanning sequences of fast scanning and repeat scanning for improving the dynamic contrast image quality, while the total data acquisition time remains almost the same.

关键词： optical coherence tomography high-resolution imaging dynamic imaging bulk-motion suppression

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Co2-Induced Switching between Mof-Based Bio-Mimic Slow Anion Channel and Proton Pump for Medical Exhalation Detection

SSRN

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SSRN 2024年

作者： Chen, Honghao Yue, Xiaorui Fan, Yifei Zheng, Bin Lv, Sitao Wang, Fengnan Gao, Yixun Li, Hao Lee, Yi-Kuen French, Patrick J. Umar Siddiqui, Ahmad M. Wang, Yao Zhou, Guofu Guangdong Provincial Key Laboratory of Optical Information Materials and Technology Institute of Electronic Paper Displays South China Academy of Advanced Optoelectronics South China Normal University Guangzhou510006 China National Center for International Research on Green Optoelectronics South China Normal University Guangzhou510006 China Department of Thoracic Oncology State Key Laboratory of Respiratory Diseases The First Affiliated Hospital of Guangzhou Medical University Guangzhou510006 China Department of Mechanical & Aerospace Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong Department of Electronic & Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong BE Lab Faculty EWI Delft University of Technology Delft2628CD Netherlands Najran University Najran11001 Saudi Arabia

Inspired by the activation mechanism of slow anion channels (SLAC1) in plants that proton pump reversibly induces plant stomata open for CO2 adsorption, a CO2-switching H+ conduction/HCO3– diffusion dual ion channel ( PCBD ion channel) was constructed by assembling γ-cyclodextrin-MOF (γ-CD-MOF) and 3,4,9,10-perylenetetracarboxylic acid (PTCA) for CO2 chemiresistive sensing. The obtained CO2 sensor exhibited high response (Rg/R0 = 1.33, 50 ppm) and selectivity, low practical limit of detection (1 ppm) and excellent consistency (94.5%) with a commercial infrared CO2 meter at room temperature. It is indicated that hydrogen bond networks in PCBD ion channel will be enlarged with the increasing of carboxylic group’s content on perylene skeleton, thereby modulating proton conductivity at molecular level and furthermore CO2 sensing performance of the composite. The PCBD ion channel-based sensor has been utilized to distinguish the exhaled CO2 concentration between lung cancer patients and healthy individuals, illustrating its promising application prospect in non-invasive diagnose. © 2024, The Authors. All rights reserved.

关键词： Carbon dioxide

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Free-electron-driven X-ray caustics from strained van der Waals materials

Free-electron-driven X-ray caustics from strained van der Wa...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Xihang Shi Yaniv Kurman Michael Shentcis Liang Jie Wong F. Javier García de Abajo Ido Kaminer Solid State Institute and Faculty of Electrical and Computer Engineering Technion – Israel Institute of Technology Haifa Israel School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore ICFO—Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Spain ICREA—Institució Catalana de Recerca i Estudis Avançats Barcelona Spain

We show that X-ray caustics and specifically X-ray Airy beams can be created from free-electron interactions with strained van der Waals materials.

ISBN: (数字)9781957171050

ISBN: (纸本)9781665466660

We show that X-ray caustics and specifically X-ray Airy beams can be created from free-electron interactions with strained van der Waals materials.

关键词： Free electron lasers X-ray lasers Lasers and electrooptics Laser beams Electrooptical waveguides

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