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Single-shot 3d widefield fluorescence imaging with a computational miniature mesoscope

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arXiv 2020年

作者： Xue, Yujia Davison, Ian G. Boas, David A. Tian, Lei Department of Electrical and Computer Engineering Boston University Massachusetts02215 United States Department of Biology Boston University Massachusetts02215 United States Department of Biomedical Engineering Boston University Massachusetts02215 United States Neurophotonics Center Boston University Massachusetts02215 United States

Fluorescence imaging is indispensable to biology and neuroscience. The need for largescale imaging in freely behaving animals has further driven the development in miniaturized microscopes (miniscopes). However, conventional microscopes / miniscopes are inherently constrained by their limited space-bandwidth-product, shallow depth-of-field, and the inability to resolve 3D distributed emitters. Here, we present a Computational Miniature Mesoscope (CM2) that overcomes these bottlenecks and enables single-shot 3D imaging across an 8 × 7-mm2 field-of-view and 2.5-mm depth-of-field, achieving 7-μm lateral and 250-μm axial resolution. Notably, the CM2 has a compact lightweight design that integrates a microlens array for imaging and an LED array for excitation in a single platform. Its expanded imaging capability is enabled by computational imaging that augments the optics by algorithms. We experimentally validate the mesoscopic 3D imaging capability on volumetrically distributed fluorescent beads and fibers. We further quantify the effects of bulk scattering and background fluorescence on phantom experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Fluorescence imaging

Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution CT scans using GAN-CIRCLE

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Deep learning based high-resolution reconstruction of trabec...

Medical Imaging 2020: biomedical Applications in Molecular, Structural, and Functional Imaging

作者： Guha, Indranil Nadeem, Syed Ahmed You, Chenyu Zhang, Xiaoliu Levy, Steven M. Wang, Ge Torner, James C. Saha, Punam K. Department of Electrical and Computer Engineering College of Engineering University of Iowa Iowa CityIA52242 United States Department of Radiology Carver College of Medicine University of Iowa Iowa CityIA52242 United States Department of Computer Science Yale University New HavenCT05620 United States Department of Preventive and Community Dentistry College of Dentistry University of Iowa Iowa CityIA52242 United States Biomedical Imaging Center BME/CBIS Rensselaer Polytechnic Institute Troy New YorkNY12180 United States Department of Epidemiology University of Iowa Iowa CityIA52242 United States

ISBN: (纸本)9781510634015

Osteoporosis is a common age-related disease characterized by reduced bone density and increased fracture-risk. Microstructural quality of trabecular bone (Tb), commonly found at axial skeletal sites and at the end of long bones, is an important determinant of bone-strength and fracture-risk. High-resolution emerging CT scanners enable in vivo measurement of Tb microstructures at peripheral sites. However, resolution-dependence of microstructural measures and wide resolution-discrepancies among various CT scanners together with rapid upgrades in technology warrant data harmonization in CT-based cross-sectional and longitudinal bone studies. This paper presents a deep learning-based method for high-resolution reconstruction of Tb microstructures from low-resolution CT scans using GAN-CIRCLE. A network was developed and evaluated using post-registered ankle CT scans of nineteen volunteers on both low- and high-resolution CT scanners. 9,000 matching pairs of low- and high-resolution patches of size 64×64 were randomly harvested from ten volunteers for training and validation. Another 5,000 matching pairs of patches from nine other volunteers were used for evaluation. Quantitative comparison shows that predicted high-resolution scans have significantly improved structural similarity index (p © 2020 SPIE

关键词： computerized tomography

Unraveling somatotopic organization in the human brain using machine learning and adaptive supervoxel-based parcellations

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NeuroImage 2021年 245卷 118710-118710页

作者： See, Kyle B. Arpin, David J. Vaillancourt, David E. Fang, Ruogu Coombes, Stephen A. J. Crayton Pruitt Family Department of Biomedical Engineering Smart Medical Informatics Learning and Evaluation Lab College of Engineering University of Florida PO Box 116131 Gainesville FL United States Laboratory for Rehabilitation Neuroscience Department of Applied Physiology and Kinesiology University of Florida PO Box 118206 Gainesville FL United States Center for Cognitive Aging and Memory McKnight Brain Institute University of Florida Gainesville United States Department of Electrical and Computer Engineering University of Florida Gainesville FL United States

In addition to the well-established somatotopy in the pre- and post-central gyrus, there is now strong evidence that somatotopic organization is evident across other regions in the sensorimotor network. This raises several experimental questions: To what extent is activity in the sensorimotor network effector-dependent and effector-independent? How important is the sensorimotor cortex when predicting the motor effector? Is there redundancy in the distributed somatotopically organized network such that removing one region has little impact on classification accuracy? To answer these questions, we developed a novel experimental approach. fMRI data were collected while human subjects performed a precisely controlled force generation task separately with their hand, foot, and mouth. We used a simple linear iterative clustering (SLIC) algorithm to segment whole-brain beta coefficient maps to build an adaptive brain parcellation and then classified effectors using extreme gradient boosting (XGBoost) based on parcellations at various spatial resolutions. This allowed us to understand how data-driven adaptive brain parcellation granularity altered classification accuracy. Results revealed effector-dependent activity in regions of the post-central gyrus, precentral gyrus, and paracentral lobule. SMA, regions of the inferior and superior parietal lobule, and cerebellum each contained effector-dependent and effector-independent representations. Machine learning analyses showed that increasing the spatial resolution of the data-driven model increased classification accuracy, which reached 94% with 1755 supervoxels. Our SLIC-based supervoxel parcellation outperformed classification analyses using established brain templates and random simulations. Occlusion experiments further demonstrated redundancy across the sensorimotor network when classifying effectors. Our observations extend our understanding of effector-dependent and effector-independent organization within the human br

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Radiologist-Level COVID-19 detection using CT scans with detail-oriented capsule networks

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arXiv 2020年

作者： Mobiny, Aryan Cicalese, Pietro A. Zare, Samira Yuan, Pengyu Abavisan, Mohammad S. Wu, Carol C. Ahuja, Jitesh de Groot, Patricia M. Nguyen, Hien V. Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States Department of Biomedical Engineering University of Houston HoustonTX77004 United States Department of Thoracic Imaging University of Texas MD Anderson Cancer Center HoustonTX77030 United States

Radiographic images offer an alternative method for the rapid screening and monitoring of Coronavirus Disease 2019 (COVID-19) patients. This approach is limited by the shortage of radiology experts who can provide a timely interpretation of these images. Motivated by this challenge, our paper proposes a novel learning architecture, called Detail-Oriented Capsule Networks (DECAPS), for the automatic diagnosis of COVID-19 from Computed Tomography (CT) scans. Our network combines the strength of Capsule Networks with several architecture improvements meant to boost classification accuracies. First, DECAPS uses an Inverted Dynamic Routing mechanism which increases model stability by preventing the passage of information from non-descriptive regions. Second, DECAPS employs a Peekaboo training procedure which uses a two-stage patch crop and drop strategy to encourage the network to generate activation maps for every target concept. The network then uses the activation maps to focus on regions of interest and combines both coarse and fine-grained representations of the data. Finally, we use a data augmentation method based on conditional generative adversarial networks to deal with the issue of data scarcity. Our model achieves 84.3% precision, 91.5% recall, and 96.1% area under the ROC curve, significantly outperforming state-of-the-art methods. We compare the performance of the DECAPS model with three experienced, well-trained thoracic radiologists and show that the architecture significantly outperforms them. While further studies on larger datasets are required to confirm this finding, our results imply that architectures like DECAPS can be used to assist radiologists in the CT scan mediated diagnosis of COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

Improved Robustness for Deep Learning-based Segmentation of Multi-Center Myocardial Perfusion MRI Datasets Using Data Adaptive Uncertainty-guided Space-time Analysis

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arXiv 2024年

作者： Yalcinkaya, Dilek M. Youssef, Khalid Heydari, Bobak Wei, Janet Merz, Noel Bairey Judd, Robert Dharmakumar, Rohan Simonetti, Orlando P. Weinsaft, Jonathan W. Raman, Subha V. Sharif, Behzad Laboratory for Translational Imaging of Microcirculation Indiana University School of Medicine IndianapolisIN United States Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN United States Krannert Cardiovascular Research Center Dept. of Medicine Indiana Univ. School of Medicine IndianapolisIN United States Stephenson Cardiac Imaging Centre Department of Cardiac Sciences University of Calgary Alberta Canada Barbra Streisand Women’s Heart Center Smidt Heart Institute Cedars-Sinai Medical Center Los AngelesCA United States Division of Cardiology Department of Medicine Duke University DurhamNC United States Weldon School of Biomedical Engineering Purdue University West LafayetteIN United States Department of Medicine Davis Heart and Lung Research Institute The Ohio State University ColumbusOH United States Division of Cardiology NY Presbyterian Hospital Weill Cornell Medical Center New YorkNY United States OhioHealth ColumbusOH United States

Background. Fully automatic analysis of myocardial perfusion MRI datasets enables rapid and objective reporting of stress/rest studies in patients with suspected ischemic heart disease. Developing deep learning techniques that can analyze multi-center datasets despite limited training data and variations in software (pulse sequence) and hardware (scanner vendor) is an ongoing challenge. Methods. Datasets from 3 medical centers acquired at 3T (n = 150 subjects;21,150 first-pass images) were included: an internal dataset (inD;n = 95) and two external datasets (exDs;n = 55) used for evaluating the robustness of the trained deep neural network (DNN) models against differences in pulse sequence (exD-1) and scanner vendor (exD-2). A subset of inD (n = 85) was used for training/validation of a pool of DNNs for segmentation, all using the same spatiotemporal U-Net architecture and hyperparameters but with different parameter initializations. We employed a space-time sliding-patch analysis approach that automatically yields a pixel-wise "uncertainty map" as a byproduct of the segmentation process. In our approach, dubbed Data Adaptive Uncertainty-Guided Space-time (DAUGS) analysis, a given test case is segmented by all members of the DNN pool and the resulting uncertainty maps are leveraged to automatically select the "best" one among the pool of solutions. For comparison, we also trained a DNN using the established approach with the same settings (hyperparameters, data augmentation, etc.). Results. The proposed DAUGS analysis approach performed similarly to the established approach on the internal dataset (Dice score for the testing subset of inD: 0.896 ± 0.050 vs. 0.890 ± 0.049;p = n.s.) whereas it significantly outperformed on the external datasets (Dice for exD-1: 0.885 ± 0.040 vs. 0.849 ± 0.065, p © 2024, CC BY.

关键词： Image segmentation

A contrast synthesized thalamic nuclei segmentation scheme using convolutional neural networks

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arXiv 2020年

作者： Umapathy, Lavanya Keerthivasan, Mahesh Bharath Zahr, Natalie M. Bilgin, Ali Saranathan, Manojkumar Electrical and Computer Engineering University of Arizona TucsonAZ United States Medical Imaging University of Arizona TucsonAZ United States Psychiatry & Behavioral Sciences Stanford University Menlo ParkCA United States Biomedical Engineering University of Arizona TucsonAZ United States

Thalamic nuclei have been implicated in several neurological diseases. White-Matter-nulled Magnetization Prepared Rapid Gradient Echo (WMn-MPRAGE) images have been shown to provide better intra-thalamic nuclear contrast compared to conventional MPRAGE images but the additional acquisition results in increased examination times. In this work, we investigated 3D Convolutional Neural Network (CNN) based techniques for thalamic nuclei parcellation from conventional MPRAGE images. Two 3D CNNs were developed and compared for thalamic nuclei parcellation using MPRAGE images: a) a native contrast segmentation (NCS) and b) a synthesized contrast segmentation (SCS) using WMn-MPRAGE images synthesized from MPRAGE images. We trained the two segmentation frameworks using MPRAGE images (n=35) and thalamic nuclei labels generated on WMn-MPRAGE images using a multi-atlas based parcellation technique. The segmentation accuracy and clinical utility were evaluated on a cohort comprising of healthy subjects and patients with alcohol use disorder (AUD) (n=45). The SCS network yielded higher Dice scores in the Medial geniculate nucleus (P=.003) and Centromedian nucleus (P=.01) with lower volume differences for Ventral anterior (P=.001) and Ventral posterior lateral (P=.01) nuclei when compared to the NCS network. A Bland-Altman analysis revealed tighter limits of agreement with lower coefficient of variation between true volumes and those predicted by the SCS network. The SCS network demonstrated a significant atrophy in Ventral lateral posterior nucleus in AUD patients compared to healthy age-matched controls (P=0.01), agreeing with previous studies on thalamic atrophy in alcoholism, whereas the NCS network showed spurious atrophy of the Ventral posterior lateral nucleus. CNN-based contrast synthesis prior to segmentation can provide fast and accurate thalamic nuclei segmentation from conventional MPRAGE images. © 2020, CC BY-NC-ND.

关键词： Convolutional neural networks

DC-UNet: Rethinking the U-Net architecture with dual channel efficient CNN for medical images segmentation

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arXiv 2020年

作者： Lou, Ange Guan, Shuyue Loew, Murray Medical Imaging and Image Analysis Laboratory Department of Electrical and Computer Engineering George Washington University Medical Center WashingtonDC United States Medical Imaging and Image Analysis Laboratory Department of Biomedical Engineering George Washington University Medical Center WashingtonDC United States

Recently, deep learning has become much more popular in computer vision area. The Convolution Neural Network (CNN) has brought a breakthrough in images segmentation areas, especially, for medical images. In this regard, U-Net is the predominant approach to medical image segmentation task. The U-Net not only performs well in segmenting multimodal medical images generally, but also in some tough cases of them. However, we found that the classical U-Net architecture has limitation in several aspects. Therefore, we applied modifications: 1) designed efficient CNN architecture to replace encoder and decoder, 2) applied residual module to replace skip connection between encoder and decoder to improve based on the-state-of-the-art U-Net model. Following these modifications, we designed a novel architecture--DC-UNet, as a potential successor to the U-Net architecture. We created a new effective CNN architecture and build the DC-UNet based on this CNN. We have evaluated our model on three datasets with tough cases and have obtained a relative improvement in performance of 2.90%, 1.49% and 11.42% respectively compared with classical U-Net. In addition, we used the Tanimoto similarity to replace the Jaccard similarity for gray-to-gray image comparisons. Copyright © 2020, The Authors. All rights reserved.

关键词： Image segmentation

Data Packet Forwarding Strategy Based on Vehicle Tracking in Named Data Networking 2

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Data Packet Forwarding Strategy Based on Vehicle Tracking in...

2nd IEEE International Conference on Hot Information-Centric Networking, HotICN 2019

作者： Zhou, Shuo Cui, Mengtian Hou, Rui Zhao, Lian College of Computer Science South-Central University for Nationalities Wuhan China Key Laboratory of Computer System State Ethnic Affairs Commission Southwest Minzu University Chengdu China Ryerson University Dept. Electrical Computer and Biomedical Engineering 350 Victoria St. Toronto Canada

ISBN: (纸本)9781728157818

A vehicular ad-hoc network (VANET) plays an important role in the development of intelligent transportation systems;named data networking (NDN) has been considered one of the promising paradigms for next-generation Internet owing to its content-centric attributes. Thus, NDN-based VANET is emerging as a technique used in intelligent transportation system (ITS). In this study, a vehicle-tracking-based data packet forwarding (VTDF) strategy is proposed to solve the data-packet-forwarding problem in a continuously changing network topology environment caused by the mobility of the vehicle. Simulation results show that VTDF outperforms other related strategies in terms of average delay, handover delay, and delivery ratio. © 2019 IEEE.

关键词： Vehicular ad hoc networks

A simplified approach for presenting the differences between ionising and non-ionising electromagnetic radiation

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Physics Education 2020年第2期55卷 1-1页

作者： Kontomaris, S.V. Malamou, A. Balogiannis, G. Antonopoulou, N. Athens Metropolitan College Faculty of Architecture Engineering and Built Environment Athens Greece Radar Systems and Remote Sensing Lab School of Electrical and Computer Engineering of National Technical University of Athens Athens Greece Biomedical Optics and Applied Biophysics Laboratory School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Department of Molecular Biology and Genetics Democritus University of Thrace Alexandroupolis Greece

Electromagnetic radiation can be classified into two major types depending on its ability to detach electrons from atoms: ionising and non-ionising. The aforementioned categorization is significant due to the effects of ionising radiation on human tissue (e.g. carcinogenesis). However, many students around the globe cannot distinguish these two types of electromagnetic radiation and as a result significant misconceptions arise regarding this topic (for example radiation emitted by mobile phones is considered by many students as ionising). Hence, in this paper a teaching approach specifically designed and implemented for the secondary education level is presented, based on the absorbed energy distribution by atoms/molecules in each radiation type. More specifically, the number of atoms/molecules that absorb equal doses of energy per unit mass can be used in order to explain the difference in the effects caused by the two abovementioned radiation types. Also, two analogies from classical mechanics are presented to provide a full insight into the energy distribution concept from a teaching perspective. © 2019 IOP Publishing Ltd.

关键词： Science Instruction Energy Magnets Scientific Concepts Misconceptions Secondary School Science Molecular Structure Radiation Teaching Methods