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arXiv 2023年

作者： Bao, Shunxing Zhu, Sichen Kolachala, Vasantha L. Remedios, Lucas W. Hwang, Yeonjoo Sun, Yutong Deng, Ruining Cui, Can Li, Yike Li, Jia Roland, Joseph T. Liu, Qi Lau, Ken S. Kugathasan, Subra Qiu, Peng Wilson, Keith T. Coburn, Lori A. Landman, Bennett A. Huo, Yuankai Department of Electrical and Computer Engineering Vanderbilt University NashvilleTN United States Department of Biomedical Engineering Georgia Institute of Technology Emory University AtlantaGA United States Division of Pediatric Gastroenterology Department of Pediatrics Pediatric Research Institute Children's Healthcare of Atlanta Emory University School of Medicine AtlantaGA United States Department of Computer Science Vanderbilt University NashvilleTN United States School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Biostatistics Vanderbilt University Medical Center NashvilleTN United States Department of Surgery Vanderbilt University Medical Center NashvilleTN United States Department of Cell and Developmental Biology Vanderbilt University School of Medicine NashvilleTN United States Division of Gastroenterology Hepatology and Nutrition Department of Medicine Vanderbilt University Medical Center NashvilleTN United States Veterans Affairs Tennessee Valley Healthcare System NashvilleTN United States Department of Otolaryngology-Head and Neck Surgery Vanderbilt University Medical Center NashvilleTN United States

Crohn's disease (CD) is a chronic and relapsing inflammatory condition that affects segments of the gastrointestinal tract. CD activity is determined by histological findings, particularly the density of neutrophils observed on Hematoxylin and Eosin stains (H&E) imaging. However, understanding the broader morphometry and local cell arrangement beyond cell counting and tissue morphology remains challenging. To address this, we characterize six distinct cell types from H&E images and develop a novel approach for the local spatial signature of each cell. Specifically, we create a 10-cell neighborhood matrix, representing neighboring cell arrangements for each individual cell. Utilizing t-SNE for non-linear spatial projection in scatter-plot and Kernel Density Estimation contour-plot formats, our study examines patterns of differences in the cellular environment associated with the odds ratio of spatial patterns between active CD and control groups. This analysis is based on data collected at the two research institutes. The findings reveal heterogeneous nearest-neighbor patterns, signifying distinct tendencies of cell clustering, with a particular focus on the rectum region. These variations underscore the impact of data heterogeneity on cell spatial arrangements in CD patients. Moreover, the spatial distribution disparities between the two research sites highlight the significance of collaborative efforts among healthcare organizations. All research analysis pipeline tools are available at https://***/MASILab/cellNN. © 2023, CC BY.

关键词： Cells

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Multi-Center CNN-Based Spine Segmentation from T2W MRI Using Small Amounts of Data

Multi-Center CNN-Based Spine Segmentation from T2W MRI Using...

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IEEE International Symposium on biomedical Imaging

作者： Satheshkumar Kaliyugarasan Magnhild H. Dagestad Evin I. Papalini Erling Andersen John-Anker Zwart Helena Brisby Hanna Hebelka Ansgar Espeland Kerstin M. Lagerstrand Alexander S. Lundervold Dept. of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Norway Dept. of Radiology MMIV Haukeland University Hospital Norway Dept. of Radiology Haukeland University Hospital Norway Dept. of Clinical Medicine University of Bergen Norway Dept. of Medical Physics and Biomedical Engineering Sahlgrenska University Hospital Sweden Inst. of Clinical Sciences The Sahlgrenska Academy at the University of Gothenburg Sweden Dept. of Clinical Engineering Haukeland University Hospital Norway Faculty of Medicine University of Oslo Dept. of Research and Innovation Division of Clinical Neuroscience Oslo University Hospital Dept. of Orthopaedics Sahlgrenska University Hospital Sweden Dept. of Radiology Sahlgrenska University Hospital Gothenburg Sweden

Segmentation of the spinal tissues on MRI is the basis for quantitative analyses, but time-consuming if done manually. In this work, we construct a pipeline for automatic vertebrae segmentation from T2w MRI scans, assessing performance and generalizability by external validation. Our study used 15 scans from one site (Haukeland University Hospital, HUH) and 10 scans from another (Sahlgrenska University Hospital, SUH). MRI experts manually delineated the vertebral bodies Th12-L5 on all the HUH data and a subset of six scans from SUH. We trained multiple convolutional neural networks, assessing the performance in an experimental design tailored to small-data contexts and also on external data. Our best model achieved a mean Dice score of 0.899. This is comparable to results in the literature, but our system required much less training data. 1 .

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Surveillance of Pathogenic Bacteria on a Food Matrix Using Machine-Learning-Enabled Paper Chromogenic Arrays

SSRN

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SSRN 2023年

作者： Jia, Zhen Luo, Yaguang Wang, Dayang Holliday, Emma Sharma, Arnav Green, Madison M. Roche, Michelle R. Thompson-Witrick, Katherine Flock, Genevieve Pearlstein, Arne J. Yu, Hengyong Zhang, Boce Food Science and Human Nutrition Department University of Florida GainesvilleFL32611 United States Environmental Microbial and Food Safety Lab and Food Quality Lab U.S. Department of Agriculture Agricultural Research Service BeltsvilleMD20705 United States Department of Electrical and Computer Engineering University of Massachusetts LowellMA01854 United States School of Medicine Duke University DurhamNC27710 United States Department of Biomedical and Nutritional Sciences University of Massachusetts LowellMA01854 United States US Army Natick Soldier Research Development and Engineering Center NatickMA01760 United States Department of Mechanical Science and Engineering University of Illinois Urbana-Champaign UrbanaIL61801 United States

Global food systems can benefit significantly from real-time monitoring of microbial food safety, a task for which tedious operations, destructive sampling, and inability to monitor multiple pathogens remain challenging. This study reports significant improvements to a paper chromogenic array sensor - machine learning (PCA-ML) methodology sensing concentrations of volatile organic compounds (VOCs) emitted on a species-specific basis by pathogens, by streamlining dye selection, sensor fabrication, database construction, and machine learning and validation. This approach enables noncontact, time-dependent, simultaneous monitoring of multiple pathogens (Listeria monocytogenes, Salmonella, and E. coli O157:H7) at levels as low as 1 log CFU/g with over 90% accuracy. The report provides theoretical and practical frameworks demonstrating that chromogenic response, including limits of detection, depends on time integrals of VOC concentrations. The paper also discusses the potential for implementing PCA-ML in the food supply chain for different food matrices and pathogens, with species- and strain-specific identification. © 2023, The Authors. All rights reserved.

关键词： Supply chains

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Mid-infrared Chemical Imaging of Intracellular Tau Fibrils using Fluorescence-guided Computational Photothermal Microscopy

arXiv

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arXiv 2023年

作者： Zhao, Jian Jiang, Lulu Matlock, Alex Xu, Yihong Zhu, Jiabei Zhu, Hongbo Tian, Lei Wolozin, Benjamin Cheng, Ji-Xin Department of Electrical and Computer Engineering Boston University BostonMA02215 United States The Picower Institute for Learning and Memory Massachusetts Institute of Technology CambridgeMA02142 United States Department of Pharmacology and Experimental Therapeutics Boston University School of Medicine BostonMA02118 United States Department of Mechanical Engineering Massachusetts Institute of Technology CambridgeMA02142 United States Department of Physics Boston University BostonMA02215 United States State Key Laboratory of Luminescence and Applications Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun130033 China Department of Biomedical Engineering Boston University BostonMA02215 United States Photonics Center Boston University BostonMA02215 United States

Amyloid proteins are associated with a broad spectrum of neurodegenerative diseases. However, it remains a grand challenge to extract molecular structure information from intracellular amyloid proteins in their native cellular environment. To address this challenge, we developed a computational chemical microscope integrating 3D mid-infrared photothermal imaging with fluorescence imaging, termed Fluorescence-guided Bond-Selective Intensity Diffraction Tomography (FBS-IDT). Based on a low-cost and simple optical design, FBS-IDT enables chemical-specific volumetric imaging and 3D site-specific mid-IR fingerprint spectroscopic analysis of tau fibrils, an important type of amyloid protein aggregates, in their intracellular environment. Label-free volumetric chemical imaging of human cells with/without seeded tau fibrils is demonstrated to show the potential correlation between lipid accumulation and tau aggregate formation. Depth-resolved mid-infrared fingerprint spectroscopy is performed to reveal the protein secondary structure of the intracellular tau fibrils. 3D visualization of the β-sheet for tau fibril structure is achieved. Copyright © 2023, The Authors. All rights reserved.

关键词： Neurodegenerative diseases

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A robust CNN framework with dual feedback feature accumulation for detecting pneumonia opacity from chest x-ray images

arXiv

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arXiv 2020年

作者： Alam, Md. Jahin Ali, Shams Nafisa Hasan, Md. Zubair Department of Electrical and Electronic Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Biomedical Engineering Bangladesh University of Engineering and Technology Dhaka Bangladesh Department of Computer Science and Engineering Military Institute of Science and Technology Dhaka Bangladesh

Pneumonia is one of the most acute respiratory diseases having remarkably high prevalence and mortality rate. Chest X-ray (CXR) has been widely utilized for the diagnosis of this disease owing to its availability, diagnostic speed and accuracy. However, even for an expert radiologist, it is quite challenging to readily determine pneumonia opacity by examining CXRs. Therefore, this study has been structured to automate the pneumonia detection process by introducing a robust deep learning framework. The proposed network comprises of Process Convolution (Pro_Conv) blocks for feature accumulation inside Dual Feedback (DF) blocks to propagate the feature maps towards a viable detection. Experimental analysis showcase: (1) the proposed network proficiently distinguishes between normal and pneumonia opacity containing CXRs with the mean accuracy, sensitivity and specificity of 97.78%, 98.84% and 95.04%, respectively;(2) the network is constructed with significantly low parameters than the traditional ImageNets to reduce memory consumption for deployment in memory constrained mobile platforms;(3) the trade-off between accuracy and number of parameters of the model outperforms the considered classical networks by a remarkable margin;and (4) the false-negatives are lower than the false-positives (both of which are low in count) which prove the model's low-fatality prediction. Hence, the proposed network can be deployed for a rapid screening of pneumonia and can act as a great assistive tool for the radiologists in the diagnosis process. Copyright © 2020, The Authors. All rights reserved.

关键词： computer aided diagnosis

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A Fog Computing Framework for Cognitive Portable Ground Penetrating Radars

A Fog Computing Framework for Cognitive Portable Ground Pene...

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2019 IEEE International Conference on Communications, ICC 2019

作者： Wu, Dalei Omwenga, Maxwell M. Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781538680889

With distributed communication, computation, and storage resources close to end users/devices, fog computing (FC) makes it very promising to develop cognitive portable ground penetrating radars (GPRs) operating intelligently and adaptively under varying sensing conditions. However both strict performance requirement and tradeoffs between communication and computation pose significant challenges. This paper presents a fog computing framework for cognitive portable GPRs. Specifically, the system architecture of an FC-enabled cognitive portable GPR is developed. Based on the identification of various involved computation tasks, an offloading policy was proposed to determine whether computation tasks should be executed locally or offloaded to the fog server. Experimental results show the efficacy of the proposed methods. The framework also provides insight into the design of cognitive Internet of things (IoT) supported by fog computing. © 2019 IEEE.

关键词： Fog computing

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Generative Models in Computational Pathology: A Comprehensive Survey on Methods, Applications, and Challenges

arXiv

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arXiv 2025年

作者： Zhang, Yuan Zhang, Xinfeng Qi, Xiaoming Wu, Xinyu Chen, Feng Yang, Guanyu Fu, Huazhu Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications [Southeast University Ministry of Education Nanjing China School of Biomedical Engineering Tsinghua University Beijing China Department of Biomedical Engineering Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore School of Information Science and Engineering Southeast University Nanjing China Department of Biostatistics Center for Global Health School of Public Health Nanjing Medical University Nanjing China Institute of High-Performance Computing Agency for Science Technology and Research Singapore

Generative modeling has emerged as a promising direction in computational pathology, offering capabilities such as data-efficient learning, synthetic data augmentation, and multimodal representation across diverse diagnostic tasks. This review provides a comprehensive synthesis of recent progress in the field, organized into four key domains: image generation, text generation, multimodal image–text generation, and other generative applications, including spatial simulation and molecular inference. By analyzing over 150 representative studies, we trace the evolution of generative architectures—from early generative adversarial networks to recent advances in diffusion models and foundation models with generative capabilities. We further examine the datasets and evaluation protocols commonly used in this domain and highlight ongoing limitations, including challenges in generating high-fidelity whole slide images, clinical interpretability, and concerns related to the ethical and legal implications of synthetic data. The review concludes with a discussion of open challenges and prospective research directions, with an emphasis on developing unified, multimodal, and clinically deployable generative systems. This work aims to provide a foundational reference for researchers and practitioners developing and applying generative models in computational pathology. © 2025, CC BY-NC-ND.

关键词： Generative adversarial networks

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Autonomous cognitive gpr based on edge computing and reinforcement learning 2

Autonomous cognitive gpr based on edge computing and reinfor...

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2nd IEEE International Conference on Industrial Internet Cloud, ICII 2019

作者： Omwenga, Maxwell M. Wu, Dalei Liang, Yu Yang, Li Huston, Dryver Xia, Tian Department of Computer Science and Engineering University of Tennessee at Chattanooga United States Department of Mechanical Engineering University of Vermont United States Department of Electrical and Biomedical Engineering University of Vermont United States

ISBN: (纸本)9781728129778

Autonomous cognitive ground penetrating radar (ACGPR), carried by drones or other robotic platforms, may perform robust and accurate subsurface object detection and recognition in varying environments based on real-time data processing and decision making. However, limited system computing resources and intelligence generating capability pose significant challenges for the operations of such systems. To address these challenges, in this paper we propose an ACGPR enabled by edge computing (EC) and reinforcement learning. Specifically, an edge computing based system architecture is presented to utilize edge resources for real-time intelligence generation. A reinforcement learning approach is developed as the decision-making model for the ACGPR to adaptively adjust its operational parameters. Simulation results show the accuracy and efficacy of the proposed ACGPR system. The framework also provides insight into the design of autonomous cognitive industrial Internet of things (IoT) supported by edge computing and machine learning. © 2019 IEEE.

关键词： Ground penetrating radar systems

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Prospectively accelerated dynamic speech MRI at 3 Tesla using a self-navigated spiral based manifold regularized scheme

arXiv

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arXiv 2022年

作者： Rusho, Rushdi Zahid Ahmed, Abdul Haseeb Kruger, Stanley Alam, Wahidul Meyer, David Howard, David Story, Brad Jacob, Mathews Lingala, Sajan Goud Roy J Carver Department of Biomedical Engineering University of Iowa Iowa city IA52242 United States Department of Electrical and Computer Engineering University of Iowa Iowa city IA52242 United States Department of Radiology University of Iowa Iowa city IA52242 United States Janette Ogg Voice Research Center Shenandoah University WinchesterVA22601 United States Department of Electronic Engineering Royal Holloway University of London United Kingdom Department of Speech Language and Hearing Sciences University of Arizona AZ85721 United States

This work develops and evaluates a self-navigated variable density spiral (VDS) based manifold regularization scheme to prospectively improve dynamic speech MRI at 3 Tesla. Short readout duration spirals (1.3 ms long) were used to minimize sensitivity to off-resonance. A custom 16-channel speech coil was used for improved parallel imaging of vocal tract structures. The manifold model leveraged similarities between frames sharing similar vocal tract postures without explicit motion binning. The self-navigating capability of VDS was leveraged to learn the Laplacian structure of the manifold. Reconstruction was posed as a SENSE-based non-local soft weighted temporal regularization scheme. Our approach was compared against view-sharing, low-rank, finite difference, extra-dimension-based sparsity reconstruction constraints. Under-sampling experiments were conducted on five volunteers performing repetitive and arbitrary speaking tasks at different speaking rates. Quantitative evaluation in terms of mean square error over moving edges were performed in a retrospective under-sampling experiment on one volunteer. For prospective under-sampling, blinded image quality evaluation in the categories of alias artifacts, spatial blurring, and temporal blurring were performed by three experts in voice research. Region of interest (ROI) analysis at articulator boundaries were performed in both the experiments to assess articulatory motion. Improved performance with manifold reconstruction constraints was observed over existing constraints. With prospective under-sampling, a spatial resolution of 2.4mm2/pixel and a temporal resolution of 17.4 ms/frame for single slice imaging, and 52.2 ms/frame for concurrent 3-slice imaging were achieved. We demonstrated implicit motion binning by analyzing the mechanics of the Laplacian matrix. Manifold regularization demonstrated superior image quality scores in reducing spatial and temporal blurring compared to all other reconstruction constraints

关键词： Navigation

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A Deep Learning Approach to 3D Segmentation of Brain Vasculature

A Deep Learning Approach to 3D Segmentation of Brain Vascula...

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Optics and the Brain, BRAIN 2019 - Part of Biophotonics Congress: Optics in the Life Sciences Congress 2019

作者： Tahir, Waleed Zhu, Jiabei Kura, Sreekanth Cheng, Xiaojun Boas, David Tian, Lei Department of Electrical and Computer Engineering Boston University United States Department of Optics and Optical Engineering University of Science and Technology of China China Department of Biomedical Engineering Boston University United States

The segmentation of blood-vessels is an important preprocessing step for the quantitative analysis of brain vasculature. We approach the segmentation task for two-photon brain angiograms using a fully convolutional 3D... 详细信息

ISBN: (纸本)9781943580545

关键词： Blood vessels

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