Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Medicine and Health Sciences (45)
- Physical Sciences and Mathematics (30)
- Medical Specialties (24)
- Computer Sciences (22)
- Biomedical Engineering and Bioengineering (16)
-
- Analytical, Diagnostic and Therapeutic Techniques and Equipment (13)
- Artificial Intelligence and Robotics (12)
- Bioimaging and Biomedical Optics (12)
- Anatomy (11)
- Life Sciences (11)
- Neurology (10)
- Theory and Algorithms (8)
- Cell and Developmental Biology (5)
- Investigative Techniques (5)
- Medical Sciences (5)
- Physics (5)
- Cancer Biology (4)
- Diseases (4)
- Oncology (4)
- Radiology (4)
- Urology (4)
- Biochemistry, Biophysics, and Structural Biology (3)
- Data Science (3)
- Diagnosis (3)
- Military and Veterans Studies (3)
- Nervous System (3)
- Public Affairs, Public Policy and Public Administration (3)
- Keyword
-
- Medical imaging (7)
- Algorithms (5)
- Feature extraction (5)
- Machine learning (5)
- Brain (4)
-
- Cancer (4)
- Computer aided diagnosis (4)
- Deep learning (4)
- Magnetic resonance imaging (4)
- Sensors (4)
- Tumors (4)
- Data modeling (3)
- Database systems (3)
- Prostate cancer (3)
- Radiomics (3)
- Sensitivity and specificity (3)
- Textures (3)
- Three dimensional (3)
- Wavelet transforms (3)
- Accuracy (2)
- Brain tumors (2)
- Cells (2)
- Classification (2)
- Computer graphics (2)
- Computer-aided detection (2)
- Computerized tomography (2)
- Convolutional neural network (2)
- Cyberbiosecurity (2)
- Datasets (2)
- Diseases (2)
Articles 1 - 30 of 67
Full-Text Articles in Biomedical
Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini
Healthcare Digital Twins: A Methodological Literature Review On Integrating Iot And Ai For Personalized Medicine And Predictive Care, Sara Shahnazinia, Mahsa Tavasoli, Abdolhossein Sarrafzadeh, Ali Karimoddini
Electrical & Computer Engineering Faculty Publications
Digital Twin (DT) technology has the potential to revolutionize healthcare delivery and enhance patient outcomes through personalized and precision medicine, simulation models for operations and interventions, and drug discovery. However, successful implementation of DTs in Internet of Things (IoT) and artificial intelligence (AI) healthcare is contingent upon addressing key challenges such as privacy, ethics, and robust data security. This paper presents a methodological literature review of DT applications in healthcare, systematically analyzing the current state of research, key enabling technologies, and implementation challenges. The review summarizes DT categorization approaches (application-based, technology-based, and real-time function-based); delineates core DT components such as …
Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng
Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng
Electrical & Computer Engineering Faculty Publications
The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …
A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian
A Method For Ultrasound Servo Tracking For Puncture Needle, Shitong Ye, Bo Yang, Hao Quan, Shan Liu, Minyu Tang, Jiawei Tian
Electrical & Computer Engineering Faculty Publications
Computer-aided surgical navigation technology helps and guides doctors to complete the operation smoothly, which simulates the whole surgical environment with computer technology, and then visualizes the whole operation link in three dimensions. At present, common image-guided surgical techniques such as computed tomography (CT) and X-ray imaging (X-ray) will cause radiation damage to the human body during the imaging process. To address this, we propose a novel Extended Kalman filter-based model that tracks the puncture needle-point using an ultrasound probe. To address the limitations of Kalman filtering methods based on position and velocity, our method of Kalman filtering uses the position …
Novel Micro-And Nanoformulations Of Paclitaxel For Targeting Metastatic Breast, Non-Small Cell Lung, And Pancreatic Cancers, Lobat Tayebi, Mehran Alavi
Novel Micro-And Nanoformulations Of Paclitaxel For Targeting Metastatic Breast, Non-Small Cell Lung, And Pancreatic Cancers, Lobat Tayebi, Mehran Alavi
Electrical & Computer Engineering Faculty Publications
Nonlinear pharmacokinetics resulting from high lipophilic and low oral bioavailability, and hypersensitivity reactions and hyperlipidemia caused by formulation by Cremophor EL have limited clinical effectiveness of paclitaxel (Taxol). In this way, there is the critical necessity of innovative drug delivery systems (DDSs) to mitigate severe side effects and overcome clinical limitations of paclitaxel. In recent years, various micro- and nanoformulations, specifically polymeric nanoparticles (NPs) and lipid NPs, have been presented and approved by the Food and Drug Administration (FDA). In addition, other nanoformulations, such as polymeric nanoparticles (NPs), micelles, liposomes, and mesoporous silica nanoparticles, have shown promising results in vitro …
Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin
Personalized Prediction Of Tumor Recurrence With Image-Guided Physics-Informed Computational Model In High-Grade Gliomas, Walia Farzana, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
High grade gliomas are infiltrating tumors characterized by their diffusive invasion and proliferative growth. Across and within patients heterogeneity of tumors makes it challenging to determine tumor spatial extent after surgical resection. Traditionally, tumor growth predictions after surgical resections rely on generalized models and population-based observations, which do not account for individual patient differences. To address this gap, we propose a personalized approach with image-guided computational model (digital twin) that incorporates physics-based modeling to predict tumor recurrence. Our digital twin involves an inverse modeling step, followed by a recurrence model that accounts for varying surgical effects. The physics-guided inverse model …
Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin
Wavelet-Based Harmonization Of Local And Global Model Shifts In Federated Learning For Histopathological Images, W. Farzana, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Federated Learning (FL) is a promising machine learning approach for development of data-driven global model using collaborative local models across multiple local institutions. However, the heterogeneity of medical imaging data is one of the challenges within FL. This heterogeneity is caused by the variation in imaging scanner protocols across institutions, which may result in weight shift among local models leading to deterioration in predictive accuracy of global model. The prevailing approaches involve applying different FL averaging techniques to enhance the performance of the global model, ignoring the distinct imaging features of the local domain. In this work, we address both …
Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim
Adversarial Training Based Domain Adaptation Of Skin Cancer Images, Syed Qasim Gilani, Muhammad Umair, Maryam Naqvi, Oge Marques, Hee-Cheol Kim
Electrical & Computer Engineering Faculty Publications
Skin lesion datasets used in the research are highly imbalanced; Generative Adversarial Networks can generate synthetic skin lesion images to solve the class imbalance problem, but it can result in bias and domain shift. Domain shifts in skin lesion datasets can also occur if different instruments or imaging resolutions are used to capture skin lesion images. The deep learning models may not perform well in the presence of bias and domain shift in skin lesion datasets. This work presents a domain adaptation algorithm-based methodology for mitigating the effects of domain shift and bias in skin lesion datasets. Six experiments were …
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Domain Adaptive Federated Learning For Multi-Institution Molecular Mutation Prediction And Bias Identification, W. Farzana, M. A. Witherow, I. Longoria, M. S. Sadique, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Deep learning models have shown potential in medical image analysis tasks. However, training a generalized deep learning model requires huge amounts of patient data that is usually gathered from multiple institutions which may raise privacy concerns. Federated learning (FL) provides an alternative to sharing data across institutions. Nonetheless, FL is susceptible to a few challenges including inversion attacks on model weights, heterogenous data distributions, and bias. This study addresses heterogeneity and bias issues for multi-institution patient data by proposing domain adaptive FL modeling using several radiomics (volume, fractal, texture) features for O6-methylguanine-DNA methyltransferase (MGMT) classification across multiple institutions. The proposed …
Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen
Distance Estimation Based On Step Frequency Using Accelerometer Data, Rami M. Al-Naimat, Khawlah M. Harahsheh, Chung-Hao Chen
Electrical & Computer Engineering Faculty Publications
In recent years, smartphone sensors have become one of the most important and easily available sensors to facilitate people's lives, especially in health care and positioning (indoor environments). However, the data coming from smartphone sensors can be distorted during the user’s movement such as irrelevant movements, walk mode, and speed of walking. This distortion (noise) impairs the estimated distance accuracy (accumulative error) which increases with increasing walking distance. In addition, the accuracy of the distance traveled is affected by the user's speed, as the speed affects the step length. This work proposes a novel approach for calculating step length in …
Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna
Recent Progress In Microrna Detection Using Integrated Electric Fields And Optical Detection Methods, Logeeshan Velmanickam, Dharmakeerthi Nawarathna
Electrical & Computer Engineering Faculty Publications
Low-cost, highly-sensitivity, and minimally invasive tests for the detection and monitoring of life-threatening diseases and disorders can reduce the worldwide disease burden. Despite a number of interdisciplinary research efforts, there are still challenges remaining to be addressed, so clinically significant amounts of relevant biomarkers in body fluids can be detected with low assay cost, high sensitivity, and speed at point-of-care settings. Although the conventional proteomic technologies have shown promise, their ability to detect all levels of disease progression from early to advanced stages is limited to a limited number of diseases. One potential avenue for early diagnosis is microRNA (miRNA). …
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Ultrasensitive Tapered Optical Fiber Refractive Index, Erem Ujah, Meimei Lai, Gymama Slaughter
Electrical & Computer Engineering Faculty Publications
Refractive index (RI) sensors are of great interest for label-free optical biosensing. A tapered optical fiber (TOF) RI sensor with micron-sized waist diameters can dramatically enhance sensor sensitivity by reducing the mode volume over a long distance. Here, a simple and fast method is used to fabricate highly sensitive refractive index sensors based on localized surface plasmon resonance (LSPR). Two TOFs (l = 5 mm) with waist diameters of 5 µm and 12 µm demonstrated sensitivity enhancement at λ = 1559 nm for glucose sensing (5-45 wt%) at room temperature. The optical power transmission decreased with increasing glucose concentration due …
Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette
Atlas-Based Shared-Boundary Deformable Multi-Surface Models Through Multi-Material And Two-Manifold Dual Contouring, Tanweer Rashid, Sharmin Sultana, Mallar Chakravarty, Michel Albert Audette
Electrical & Computer Engineering Faculty Publications
This paper presents a multi-material dual “contouring” method used to convert a digital 3D voxel-based atlas of basal ganglia to a deformable discrete multi-surface model that supports surgical navigation for an intraoperative MRI-compatible surgical robot, featuring fast intraoperative deformation computation. It is vital that the final surface model maintain shared boundaries where appropriate so that even as the deep-brain model deforms to reflect intraoperative changes encoded in ioMRI, the subthalamic nucleus stays in contact with the substantia nigra, for example, while still providing a significantly sparser representation than the original volumetric atlas consisting of hundreds of millions of voxels. The …
Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer
Commentary On Healthcare And Disruptive Innovation, Hilary Finch, Affia Abasi-Amefon, Woosub Jung, Lucas Potter, Xavier-Lewis Palmer
Electrical & Computer Engineering Faculty Publications
Exploits of technology have been an issue in healthcare for many years. Many hospital systems have a problem with “disruptive innovation” when introducing new technology. Disruptive innovation is “an innovation that creates a new market by applying a different set of values, which ultimately overtakes an existing market” (Sensmeier, 2012). Modern healthcare systems are historically slow to accept new technological advancements. This may be because patient-based, provider-based, or industry-wide decisions are tough to implement, giving way to dire consequences. One potential consequence is that healthcare providers may not be able to provide the best possible care to patients. For example, …
Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco
Virtual Surgical Planning In Craniomaxillofacial Surgery: A Structured Review, Kaye Verlarde, Rentor Cafino, Armando Isla Jr., Karen Mae Ty, Xavier-Lewis Palmer, Lucas Potter, Larry Nadorra, Luchin Valrian Pueblos, Lemuel Clark Velasco
Electrical & Computer Engineering Faculty Publications
Craniomaxillofacial (CMF) surgery is a challenging and very demanding field that involves the treatment of congenital and acquired conditions of the face and head. Due to the complexity of the head and facial region, various tools and techniques were developed and utilized to aid surgical procedures and optimize results. Virtual Surgical Planning (VSP) has revolutionized the way craniomaxillofacial surgeries are planned and executed. It uses 3D imaging computer software to visualize and simulate a surgical procedure. Numerous studies were published on the usage of VSP in craniomaxillofacial surgery. However, the researchers found inconsistency in the previous literature which prompted the …
Social-Engineering, Bio-Economies, And Nation-State Ontological Security: A Commentary, Brandon Griffin, Keitavius Alexander, Xavier-Lewis Palmer, Lucas Potter
Social-Engineering, Bio-Economies, And Nation-State Ontological Security: A Commentary, Brandon Griffin, Keitavius Alexander, Xavier-Lewis Palmer, Lucas Potter
Electrical & Computer Engineering Faculty Publications
Biocybersecurity is an evolving discipline that aims to identify the gaps and risks associated with the convergence of Biology (the science of life and living organisms) and cybersecurity (the science, study, and theory of cyberspace and cybernetics) to protect the bioeconomy. The biological industries’ increased reliance on digitization, automation, and computing power has resulted in benefits for the scientific community, it has simultaneously multiplied the risk factors associated with industrial espionage and the protection of data both commercial and proprietary. The sensitive and potentially destructive power of this data and its access inherently poses a risk to the national and …
Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)
Opioid Use Disorder Prediction Using Machine Learning Of Fmri Data, A. Temtam, Liangsuo Ma, F. Gerard Moeller, M. S. Sadique, K. M. Iftekharuddin, Khan M. Iftekharuddin (Ed.), Weijie Chen (Ed.)
Electrical & Computer Engineering Faculty Publications
According to the Centers for Disease Control and Prevention (CDC) more than 932,000 people in the US have died since 1999 from a drug overdose. Just about 75% of drug overdose deaths in 2020 involved Opioid, which suggests that the US is in an Opioid overdose epidemic. Identifying individuals likely to develop Opioid use disorder (OUD) can help public health in planning effective prevention, intervention, drug overdose and recovery policies. Further, a better understanding of prediction of overdose leading to the neurobiology of OUD may lead to new therapeutics. In recent years, very limited work has been done using statistical …
Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)
Transfer Learning Using Infrared And Optical Full Motion Video Data For Gender Classification, Alexander M. Glandon, Joe Zalameda, Khan M. Iftekharuddin, Gabor F. Fulop (Ed.), David Z. Ting (Ed.), Lucy L. Zheng (Ed.)
Electrical & Computer Engineering Faculty Publications
This work is a review and extension of our ongoing research in human recognition analysis using multimodality motion sensor data. We review our work on hand crafted feature engineering for motion capture skeleton (MoCap) data, from the Air Force Research Lab for human gender followed by depth scan based skeleton extraction using LIDAR data from the Army Night Vision Lab for person identification. We then build on these works to demonstrate a transfer learning sensor fusion approach for using the larger MoCap and smaller LIDAR data for gender classification.
Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides
Special Section Editorial: Artificial Intelligence For Medical Imaging In Clinical Practice, Claudia Mello-Thoms, Karen Drukker, Sian Taylor-Phillips, Khan Iftekharuddin, Marios Gavrielides
Electrical & Computer Engineering Faculty Publications
This editorial introduces the JMI Special Section on Artificial Intelligence for Medical Imaging in Clinical Practice.
Electron Beam Treatment For The Removal Of 1,4-Dioxane In Water And Wastewater, Robert Pearce, Xi Li, John Vennekate, Gianluigi Ciovati, Charles Bott
Electron Beam Treatment For The Removal Of 1,4-Dioxane In Water And Wastewater, Robert Pearce, Xi Li, John Vennekate, Gianluigi Ciovati, Charles Bott
Electrical & Computer Engineering Faculty Publications
Electron beam (e-beam) treatment uses accelerated electrons to form oxidizing and reducing radicals when applied to water without the use of external chemicals. In this study, electron beam treatment was used to degrade 1,4-dioxane in several water matrices. Removal improved in the progressively cleaner water matrices and removals as high as 94% to 99% were observed at a dose of 2.3 kGy in secondary effluent. 1,4-dioxane removal was confirmed to be primarily through hydroxyl radical oxidation. The calculated electrical energy per order was found to be 0.53, 0.26, and 0.08 kWh/m3/order for secondary effluent (Avg. total organic carbon …
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Toward Real-Time, Robust Wearable Sensor Fall Detection Using Deep Learning Methods: A Feasibility Study, Haben Yhdego, Christopher Paolini, Michel Audette
Electrical & Computer Engineering Faculty Publications
Real-time fall detection using a wearable sensor remains a challenging problem due to high gait variability. Furthermore, finding the type of sensor to use and the optimal location of the sensors are also essential factors for real-time fall-detection systems. This work presents real-time fall-detection methods using deep learning models. Early detection of falls, followed by pneumatic protection, is one of the most effective means of ensuring the safety of the elderly. First, we developed and compared different data-segmentation techniques for sliding windows. Next, we implemented various techniques to balance the datasets because collecting fall datasets in the real-time setting has …
Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin
Radiomic Texture Feature Descriptor To Distinguish Recurrent Brain Tumor From Radiation Necrosis Using Multimodal Mri, M. S. Sadique, A. Temtam, E. Lappinen, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Despite multimodal aggressive treatment with chemo-radiation-therapy, and surgical resection, Glioblastoma Multiforme (GBM) may recur which is known as recurrent brain tumor (rBT), There are several instances where benign and malignant pathologies might appear very similar on radiographic imaging. One such illustration is radiation necrosis (RN) (a moderately benign impact of radiation treatment) which are visually almost indistinguishable from rBT on structural magnetic resonance imaging (MRI). There is hence a need for identification of reliable non-invasive quantitative measurements on routinely acquired brain MRI scans: pre-contrast T1-weighted (T1), post-contrast T1-weighted (T1Gd), T2-weighted (T2), and T2 Fluid Attenuated Inversion Recovery (FLAIR) that can …
Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin
Uncertainty Estimation In Classification Of Mgnt Using Radiogenomics For Glioblastoma Patients, W. Farzana, Z. A. Shboul, A. Temtam, K. M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Glioblastoma Multiforme (GBM) is one of the most malignant brain tumors among all high-grade brain cancers. Temozolomide (TMZ) is the first-line chemotherapeutic regimen for glioblastoma patients. The methylation status of the O6-methylguanine-DNA-methyltransferase (MGMT) gene is a prognostic biomarker for tumor sensitivity to TMZ chemotherapy. However, the standardized procedure for assessing the methylation status of MGMT is an invasive surgical biopsy, and accuracy is susceptible to resection sample and heterogeneity of the tumor. Recently, radio-genomics which associates radiological image phenotype with genetic or molecular mutations has shown promise in the non-invasive assessment of radiotherapeutic treatment. This study proposes a machine-learning framework …
Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel
Qu-Brats: Miccai Brats 2020 Challenge On Quantifying Uncertainty In Brain Tumor Segmentation - Analysis Of Ranking Scores And Benchmarking Results, Raghav Mehta, Angelos Filos, Ujjwal Baid, Chiharu Sako, Richard Mckinley, Michael Rebsamen, Katrin Dätwyler, Raphael Meier, Piotr Radojewski, Gowtham Krishnan Murugesan, Sahil Nalawade, Chandan Ganesh, Ben Wagner, Fang F. Yu, Baowei Fei, Ananth J. Madhuranthakam, Joseph A. Maldjian, Laura Daza, Catalina Gómez, Pablo Arbeláez, Chengliang Dai, Shuo Wang, Hadrien Reynaud, Yuan-Han Mo, Elsa Angelini, Yike Guo, Wenjia Bai, Subhashis Banerjee, Lin-Min Pei, Murat Ak, Sarahi Rosas-González, Ilyess Zemmoura, Clovis Tauber, Minh H. Vu, Tufve Nyholm, Tommy Löfstedt, Laura Mora Ballestar, Veronica Vilaplana, Hugh Mchugh, Gonzalo Maso Talou, Alan Wang, Jay Patel, Ken Chang, Katharina Hoebel, Mishka Gidwani, Nishanth Arun, Sharut Gupta, Mehak Aggarwal, Praveer Singh, Elizabeth R. Gerstner, Jayashree Kalpathy-Cramer, Nicholas Boutry, Alexis Huard, Lasitha Vidyaratne, Md. Monibor Rahman, Khan M. Iftekharuddin, Joseph Chazalon, Elodie Puybareau, Guillaume Tochon, Jun Ma, Mariano Cabezas, Xavier Llado, Arnau Oliver, Liliana Valencia, Sergi Valverde, Mehdi Amian, Mohammadreza Soltaninejad, Andriy Myronenko, Ali Hatamizadeh, Xue Feng, Quan Dou, Nicholas Tustison, Craig Meyer, Nisarg A. Shah, Sanjay Talbar, Marc-André Weber, Abhishek Mahajan, Andras Jakab, Roland Wiest, Hassan M. Fathallah-Shaykh, Arash Nazeri, Mikhail Milchenko1, Daniel Marcus, Aikaterini Kotrotsou, Rivka Colen, John Freymann, Justin Kirby, Christos Davatzikos, Bjoern Menze, Spyridon Bakas, Yarin Gal, Tal Arbel
Electrical & Computer Engineering Faculty Publications
Deep learning (DL) models have provided the state-of-the-art performance in a wide variety of medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder the translation of DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties, could enable clinical review of the most uncertain regions, thereby building trust and paving the way towards clinical translation. Recently, a number of uncertainty estimation methods have been introduced for DL medical image segmentation tasks. …
Design Of A 10 Mev Beamline At The Upgraded Injector Test Facility For E-Beam Irradiation, Xi Li, Helmut Baumgart, Gianluigi Ciovati, F.E. Hannon, S. Wang
Design Of A 10 Mev Beamline At The Upgraded Injector Test Facility For E-Beam Irradiation, Xi Li, Helmut Baumgart, Gianluigi Ciovati, F.E. Hannon, S. Wang
Electrical & Computer Engineering Faculty Publications
Electron beam irradiation near 10 MeV is suitable for wastewater treatment. The Upgraded Injector Test Facility (UITF) at Jefferson Lab is a CW superconducting linear accelerator capable of providing an electron beam of energy up to 10 MeV and up to 100 µA current. This contribution presents the beam transport simulations for a beamline to be used for the irradiation of wastewater samples at the UITF. The simulations were done using the code General Particle Tracer with the goal of obtaining an 8 MeV electron beam of radius (3-σ) of ~2.4 cm. The achieved energy spread is ~74.5 keV. The …
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Joint Modeling Of Rnaseq And Radiomics Data For Glioma Molecular Characterization And Prediction, Zeina A. Shboul, Norou Diawara, Arastoo Vossough, James Y. Chen, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
RNA sequencing (RNAseq) is a recent technology that profiles gene expression by measuring the relative frequency of the RNAseq reads. RNAseq read counts data is increasingly used in oncologic care and while radiology features (radiomics) have also been gaining utility in radiology practice such as disease diagnosis, monitoring, and treatment planning. However, contemporary literature lacks appropriate RNA-radiomics (henceforth, radiogenomics) joint modeling where RNAseq distribution is adaptive and also preserves the nature of RNAseq read counts data for glioma grading and prediction. The Negative Binomial (NB) distribution may be useful to model RNAseq read counts data that addresses potential shortcomings. …
The Resistive Barrier Discharge: A Brief Review Of The Device And Its Biomedical Applications, Mounir Laroussi
The Resistive Barrier Discharge: A Brief Review Of The Device And Its Biomedical Applications, Mounir Laroussi
Electrical & Computer Engineering Faculty Publications
This paper reviews the principles behind the design and operation of the resistive barrier discharge, a low temperature plasma source that operates at atmospheric pressure. One of the advantages of this plasma source is that it can be operated using either DC or AC high voltages. Plasma generated by the resistive barrier discharge has been used to efficiently inactivate pathogenic microorganisms and to destroy cancer cells. These biomedical applications of low temperature plasma are of great interest because in recent times bacteria developed increased resistance to antibiotics and because present cancer therapies often are accompanied by serious side effects. Low …
Deep Neural Network Analysis Of Pathology Images With Integrated Molecular Data For Enhanced Glioma Classification And Grading, Linmin Pei, Karra A. Jones, Zeina A. Shboul, James Y. Chen, Khan M. Iftekharuddin
Deep Neural Network Analysis Of Pathology Images With Integrated Molecular Data For Enhanced Glioma Classification And Grading, Linmin Pei, Karra A. Jones, Zeina A. Shboul, James Y. Chen, Khan M. Iftekharuddin
Electrical & Computer Engineering Faculty Publications
Gliomas are primary brain tumors that originate from glial cells. Classification and grading of these tumors is critical to prognosis and treatment planning. The current criteria for glioma classification in central nervous system (CNS) was introduced by World Health Organization (WHO) in 2016. This criteria for glioma classification requires the integration of histology with genomics. In 2017, the Consortium to Inform Molecular and Practical Approaches to CNS Tumor Taxonomy (cIMPACT-NOW) was established to provide up-to-date recommendations for CNS tumor classification, which in turn the WHO is expected to adopt in its upcoming edition. In this work, we propose a novel …
Low-Temperature Gas Plasma Combined With Antibiotics For The Reduction Of Methicillin-Resistant Staphylococcus Aureus Biofilm Both In Vitro And In Vivo, Li Guo, Lu Yang, Yu Qi, Gulimire Niyazi, Jianbao Zheng, Ruobing Xu, Xusong Chen, Jingye Zhang, Wang Xi, Dingxin Liu, Xiaohua Wang, Hailan Chen, Michael G. Kong
Low-Temperature Gas Plasma Combined With Antibiotics For The Reduction Of Methicillin-Resistant Staphylococcus Aureus Biofilm Both In Vitro And In Vivo, Li Guo, Lu Yang, Yu Qi, Gulimire Niyazi, Jianbao Zheng, Ruobing Xu, Xusong Chen, Jingye Zhang, Wang Xi, Dingxin Liu, Xiaohua Wang, Hailan Chen, Michael G. Kong
Electrical & Computer Engineering Faculty Publications
Biofilm infections in wounds seriously delay the healing process, and methicillin-resistant Staphylococcus aureus is a major cause of wound infections. In addition to inactivating micro-organisms, low-temperature gas plasma can restore the sensitivity of pathogenic microbes to antibiotics. However, the combined treatment has not been applied to infectious diseases. In this study, low-temperature gas plasma treatment promoted the effects of different antibiotics on the reduction of S. aureus biofilms in vitro. Low-temperature gas plasma combined with rifampicin also effectively reduced the S. aureus cells in biofilms in the murine wound infection model. The blood and histochemical analysis demonstrated the biosafety of …
Plasma-Treated Solutions (Pts) In Cancer Therapy, Hiromasa Tanaka, Sander Bekeschus, Dayun Yan, Masaru Hori, Michael Keidar, Mounir Laroussi
Plasma-Treated Solutions (Pts) In Cancer Therapy, Hiromasa Tanaka, Sander Bekeschus, Dayun Yan, Masaru Hori, Michael Keidar, Mounir Laroussi
Electrical & Computer Engineering Faculty Publications
Cold physical plasma is a partially ionized gas generating various reactive oxygen and nitrogen species (ROS/RNS) simultaneously. ROS/RNS have therapeutic effects when applied to cells and tissues either directly from the plasma or via exposure to solutions that have been treated beforehand using plasma processes. This review addresses the challenges and opportunities of plasma-treated solutions (PTSs) for cancer treatment. These PTSs include plasma-treated cell culture media in experimental research as well as clinically approved solutions such as saline and Ringer’s lactate, which, in principle, already qualify for testing in therapeutic settings. Several types of cancers were found to succumb to …
Monopolar Gene Electrotransfer Enhances Plasmid Dna Delivery To Skin, Anna Bulysheva, Loree Heller, Michael Francis, Frency Varghese, Carly Boye, Richard Heller
Monopolar Gene Electrotransfer Enhances Plasmid Dna Delivery To Skin, Anna Bulysheva, Loree Heller, Michael Francis, Frency Varghese, Carly Boye, Richard Heller
Electrical & Computer Engineering Faculty Publications
A novel monopolar electroporation system and methodologies were developed for in vivo electroporation intended for potential clinical applications such as gene therapy. We hypothesized that an asymmetric anode/cathode electrode applicator geometry could produce favorable electric fields for electroporation, without the typical drawback associated with traditional needle and parallel plate geometries. Three monopolar electrode applicator prototypes were built and tested for gene delivery of reporter genes to the skin in a guinea pig model. Gene expression was evaluated in terms of kinetics over time and expression distribution within the treatment site. Different pulsing parameters, including pulse amplitude, pulse duration, and pulse …