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arXiv 2023年

作者： Lucassen, Ruben T. Jafari, Mohammad H. Duggan, Nicole M. Jowkar, Nick Mehrtash, Alireza Fischetti, Chanel Bernier, Denie Prentice, Kira Duhaime, Erik P. Jin, Mike Abolmaesumi, Purang Heslinga, Friso G. Veta, Mitko Duran-Mendicuti, Maria A. Frisken, Sarah Shyn, Paul B. Golby, Alexandra J. Boyer, Edward Wells, William M. Goldsmith, Andrew J. Kapur, Tina Department of Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States The Department of Biomedical Engineering Eindhoven University of Technology Eindhoven5612 Netherlands The Department of Emergency Medicine Brigham and Women’s Hospital BostonMA02115 United States Centaur Labs BostonMA02116 United States The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV5T 1Z4 Canada The Departments of Neurosurgery and Radiology Brigham and Women’s Hospital Harvard Medical School BostonMA02115 United States

Lung ultrasound (LUS) is an important imaging modality used by emergency physicians to assess pulmonary congestion at the patient bedside. B-line artifacts in LUS videos are key findings associated with pulmonary congestion. Not only can the interpretation of LUS be challenging for novice operators, but visual quantification of B-lines remains subject to observer variability. In this work, we investigate the strengths and weaknesses of multiple deep learning approaches for automated B-line detection and localization in LUS videos. We curate and publish, BEDLUS, a new ultrasound dataset comprising 1,419 videos from 113 patients with a total of 15,755 expert-annotated B-lines. Based on this dataset, we present a benchmark of established deep learning methods applied to the task of B-line detection. To pave the way for interpretable quantification of B-lines, we propose a novel "single-point" approach to B-line localization using only the point of origin. Our results show that (a) the area under the receiver operating characteristic curve ranges from 0.864 to 0.955 for the benchmarked detection methods, (b) within this range, the best performance is achieved by models that leverage multiple successive frames as input, and (c) the proposed single-point approach for B-line localization reaches an F1-score of 0.65, performing on par with the inter-observer agreement. The dataset and developed methods can facilitate further biomedical research on automated interpretation of lung ultrasound with the potential to expand the clinical utility. Copyright © 2023, The Authors. All rights reserved.

关键词： COVID-19

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Assessing Dynamic Cerebral Autoregulation in Patients with Sickle Cell Disease Using Near-Infrared Spectroscopy and Paced Breathing

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BLOOD 2020年 136卷 57-58页

作者： Wood, Sossena Fanta, Abeselom Dosunmu-Ogunbi, Atinuke Ruesch, Alexander Jonassaint, Jude Huppert, Theodore Novelli, Enrico M. Kainerstorfer, Jana Department of Biomedical Engineering Carnegie Mellon University Pittsburgh PA Carnegie Mellon Neuroscience Institute Carnegie Mellon University Pittsburgh PA University of Pittsburgh School of Medicine Pittsburgh PA Academic Research Center for Hematology (ARCH) University of Pittsburgh Pittsburgh PA Department of Electrical and Computer Engineering University of Pittsburgh Pittsburgh PA Heart Lung and Blood Vascular Medicine Institute University of Pittsburgh Pittsburgh PA

Introduction. Sickle cell disease (SCD) is caused by hemoglobin S polymerization leading to vaso-occlusion, small vessel disease, and reduced tissue oxygen saturation. Noninvasive assessments and screening of small vessel disease and decreased oxygen saturation in select organs, like the brain, are poorly developed. Cerebral autoregulation, a mechanism that ensures cerebral blood flow (CBF) remains constant with changes in mean arterial pressure, is impaired with and is a marker of small vessel disease. Frequency-domain near-infrared spectroscopy (FD-NIRS) can be used as a noninvasive technique to measure cerebral autoregulation by recording cerebral microvascular changes in hemoglobin concentration that are related to cerebral blood flow. Transfer function analysis is a method to noninvasively quantify the relationship between measured physiologic signals, and can be used to estimate cerebral autoregulation. In this study, we hypothesized patients with SCD have impaired cerebral autoregulation and reduced tissue oxygen saturation, which we evaluated via FD-NIRS with transfer function analysis, as compared to healthy controls. Methods. All patients provided informed consent under an IRB protocol approved by the University of Pittsburgh and Carnegie Mellon University. Patients with SCD (n = 11) and healthy, race-matched controls (n =14) were enrolled. All participants sat upright in a chair with probes of a commercial multi-distance FD-NIRS apparatus applied to their forehead in a dark room (Fig. 1A). We used paced breathing to dynamically induce controlled changes in the microvascular tone at 0.1, 0.125, and 0.1667 Hz, spanning normal to more challenging breathing rates, with a visual metronome guiding each paced breathing session (Fig.1.B). To validate each breathing pace, we recorded the participants’ respiratory rate via a belt placed around their chest, and their mean arterial pressure through a finger and arm cuff attached to a beat-to-beat finger plethysmograp

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Deep learning of volumetric 3D CNN for fMRI in Alzheimer's disease classification

Deep learning of volumetric 3D CNN for fMRI in Alzheimer's d...

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

作者： Parmar, Harshit S. Nutter, Brian Long, Rodney Antani, Sameer Mitra, Sunanda Department of Electrical and Computer Engineering Texas Tech University Box 43102 LubbockTX79409-3102 United States Lister Hill National Center for Biomedical Communications National Library of Medicine National Institute of Health BethesdaMD20894 United States

ISBN: (纸本)9781510634015

Functional magnetic resonance imaging has a potential to provide insight into early detectors or biomarkers for various neurological disorders. With the advent of recent developments in deep learning, it may be possible to extract detailed information from neuroimaging data that is difficult to acquire using traditional techniques. Here we propose one such deep learning approach that makes use of a 3D Convolutional Neural Network to predict the onset of Alzheimer's disease even in a single subject based on resting state fMRI data. This approach extracts both spatial and temporal features from the 4D volume and eliminates the traditional complicated steps of feature extraction. In our experiments, a relatively simple deep learning architecture yields high performance in Alzheimer's disease classification. This illustrates the possibility of using volumetric feature extractors and classifiers as a tool to obtain biomarkers for neurological disorders and another step towards use of clinical fMRI. © 2020 SPIE

关键词： Biomarkers

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Design Space Exploration of Embedded Applications on Heterogeneous CPU-GPU Platforms

Design Space Exploration of Embedded Applications on Heterog...

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2019 International Conference on High Performance Computing and Simulation, HPCS 2019

作者： Siddiqui, Abdullah Khan, Gul N. Ryerson University Electrical Computer and Biomedical Engineering TorontoONM5B2K3 Canada

ISBN: (纸本)9781728144849

CPU-GPU platforms possess the potential of enhancing the performance of applications through some unique and diverse capabilities of both CPU-GPU devices. As a result, the methodologies for CPU/GPU system design space exploration for various applications are now considerably more challenging on these heterogeneous platforms. In this paper, we present a heuristic algorithm for partitioning the computation of applications between a CPU and GPU, while satisfying the user-defined constraints. Our methodology leverages the SIMD-related computing and hierarchical memory model of GPUs to optimize application mapping and allocation to CPU-GPU systems. The algorithm partitions the application, which is specified as a Directed Acyclic Graph (DAG), for a CPU-GPU platform to meet the objectives specified by the user. The effectiveness of our methodology is demonstrated by efficiently partitioning and executing MJPEG decoder and benchmark applications on a CPU-GPU system. © 2019 IEEE.

关键词： Graphics processing unit

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Caco-2 Cell-Derived Biomimetic Electrochemical Biosensor for Cholera Toxin Detection

SSRN

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SSRN 2022年

作者： Kim, Yonghwan Lee, Dongtak Seo, Youngjun Jung, Hyo Gi Jang, Jae Won Park, Dongsung Kim, Insu Lee, Gyudo Hwang, Kyo Seon Kim, Seung-Hyun Lee, Sang Won Lee, Jeong Hoon Yoon, Dae Sung School of Biomedical Engineering Korea University Seoul02841 Korea Republic of Interdisciplinary Program in Precision Public Health Korea University Seoul02841 Korea Republic of Department of Clinical Pharmacology and Therapeutics College of Medicine Kyung Hee University Seoul02447 Korea Republic of Department of Biotechnology and Bioinformatics Korea University Sejong30019 Korea Republic of Interdisciplinary Graduate Program for Artificial Intelligence Smart Convergence Technology Korea University Sejong30019 Korea Republic of School of Engineering Brown University ProvidenceRI02912 United States Department of Electrical Engineering and Computer Science University of California IrvineCA92697 United States Department of Electrical Engineering Kwangwoon University Seoul01897 Korea Republic of Astrion Inc Seoul02841 Korea Republic of

Cholera is a highly contagious and lethal waterborne disease induced by an infection with Vibrio cholerae (V. cholerae) secreting cholera toxin (CTx). Cholera toxin subunit B (CTxB) from the CTx specifically binds with monosialo-tetra-hexosyl-ganglioside (GM1) found on the exterior cell membrane of an enterocyte. Owing to the pathological process of CTx, a biomimetic electrochemical biosensor (CCB) was developed by applying GM1-expressing Caco-2 cell membrane (CCM) on the electrode surface of the biosensor for CTx detection. Briefly, the electrode surface was functionalized with CCM using the vesicle fusion method. The specific binding between GM1 and CTxB on the electrode surface was verified using confocal laser scanning microscopy and atomic force microscopy. The CTxB detection performances of the CCB, including sensitivity, selectivity, and long-term stability, were determined using electrochemical impedance spectroscopy (EIS). Based on the results, the CCB had an excellent limit of detection of ~11.46 nM and a detection range spanning 100 ng/mL - 1 mg/mL. Further, the CCB had high selectivity for the CTxB compared to different interfering molecules, which are abundant constituents of intestinal fluid and fecal matter in the human body. The long-term stability of the CCBs was also verified for 3 weeks using EIS. Overall, the CCB was found to have an excellent potential for application to point-of-care and cost-effective testing for CTxB detection in developing countries. © 2022, The Authors. All rights reserved.

关键词： Electrochemical impedance spectroscopy

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Differentiating COVID-19 Cases from Others by an Anatomy Similarity-Inspired Sensitive Merit from CT Images

Differentiating COVID-19 Cases from Others by an Anatomy Sim...

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2020 IEEE Nuclear Science Symposium and Medical Imaging Conference, NSS/MIC 2020

作者： Gao, Yongfeng Pomeroy, Marc Cao, Weiguo Lu, Siming Han, Fangfang Wang, Luhao Chen, Mu Sun, Junqi Xie, Hong Liang, Zhengrong The Department of Radiology Stony Brook University Stony Brook NY11794 United States The School of Biomedical Engineering Southern Medical University Guangdong Guangzhou510515 China The School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA16802 United States Innovision Imaging Laboratory INC DurhamNC27709 United States Duke Kunshan University Kunshan Jiangsu215316 China The Department of Radiology Yuebei People's Hospital Guangdong Shaoguan710049 China The Department of Radiology Biomedical Engineering Computer Science and Electrical Engineering Stony Brook University Stony Brook NY11794 United States The Department of Radiology The Affiliated Hospital of Guizhou Medical University Guiyang550004 China

ISBN: (纸本)9781728176932

Computed tomography (CT) of COVID-19 manifests a relatively global effect through the whole lungs, like peripheral ground glass, consolidation, reticular pattern, nodules etc. This characteristic effect renders the difficulties in differentiating COVID-19 from the normal body or other lung diseases by CT. This work presents a novel method to relieve the difficulties by reducing the global effect through the 3D whole lung volume into 2D-like domain. The hypothesis is that the lung tissue shares the similar anatomic structure within a small lung sub-volume for normal subjects. Therefore, the anatomic land-markers along the z-axis, denoted as Lung Marks are used to eliminate axial variable. Our experiments indicated that 30 Lung Marks are sufficient to eliminate the axial variable. The method computes texture measures from each 2D-like volumetric data and maps the measures on to the corresponding Lung Mark, resulting in a profile along the z-axis. The difference of the profiles between two different abnormalities is the proposed sensitive merit to differentiate COVID-19 cases from others in CT images. 48 COVID-19 cases and 48 normal screening cases were used to test the effectiveness of the proposed sensitive merit. Intensity and gradient based texture descriptors were computed from each axial cross image at the corresponding Lung Mark along the z-axis. Euclidean, Jaccard and Dice distances are calculated to generate the profiles of the proposed sensitive merit. Consistent results are observed across texture descriptor types and distance types in the texture measure between the normal and COVID-19 subjects. Uneven Profiles demonstrate the variation along the z-axis. With Lung Mark, the variation of texture descriptor has been reduced prominently. The Gradient based descriptor is more sensitive. Individual Haralick features analysis shows the 2nd and 10th dimensions are most distinguishable. © 2020 IEEE

关键词： COVID-19

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Deterministic Learning with Probabilistic Analysis on Human-Robot Shared Contro

Deterministic Learning with Probabilistic Analysis on Human-...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Xiaotian Chen Paolo Stegagno Chengzhi Yuan Department of Mechanical Industrial and Systems Engineering University of Rhode Island Kingston USA Department of Electrical Computer and Biomedical Engineering University of Rhode Island Kingston USA

ISBN: (数字)9781728167947

ISBN: (纸本)9781728167954

In this paper, we present a novel method for controlling an unmanned ground vehicle (UGV) by using a new machine learning technique, Deterministic Learning (DL). With DL the robot is able to learn and recognize four specifically designed body gestures, which represent four corresponding moving directions (i.e., left, right, forward, and backward) of the controlled UGV. A Kinect camera is employed to collect human body skeleton data of a user. Eight specifically-designed features are extracted and utilized to train radial basis function neural networks (RBFNNs). The dynamics of the human arm waving motion is guaranteed to be accurately identified, represented, and stored as an RBFNN model with converged constant NN weights, which facilitates rapid recognition in the online identification phase. However, learning time of and storage space of RBFNNs grow exponentially with the number of features. In order drastically reduce required computations and storage space, we propose to split the features in subgroups, and use each subgroup to learn a smaller independent. In the online identification phase, the trained RBFNNs are used to analyze and identify any new incoming gestures. The identification results of all RBFNNs are then fused together following a probabilistic approach, and the gestures of the user are interpreted as commands for the UGV.

关键词： Feature extraction Probabilistic logic Data mining Dynamics Artificial neural networks Robots

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MONAI: An open-source framework for deep learning in healthcare

arXiv

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arXiv 2022年

作者： Cardoso, M. Jorge Li, Wenqi Brown, Richard Ma, Nic Kerfoot, Eric Wang, Yiheng Murrey, Benjamin Myronenko, Andriy Zhao, Can Yang, Dong Nath, Vishwesh He, Yufan Xu, Ziyue Hatamizadeh, Ali Zhu, Wentao Liu, Yun Zheng, Mingxin Tang, Yucheng Yang, Isaac Zephyr, Michael Hashemian, Behrooz Alle, Sachidanand Darestani, Mohammad Zalbagi Budd, Charlie Modat, Marc Vercauteren, Tom Wang, Guotai Li, Yiwen Hu, Yipeng Fu, Yunguan Gorman, Benjamin Johnson, Hans Genereaux, Brad Erdal, Barbaros S. Gupta, Vikash Diaz-Pinto, Andres Dourson, Andre Maier-Hein, Lena Jaeger, Paul F. Baumgartner, Michael Kalpathy-Cramer, Jayashree Flores, Mona Kirby, Justin Cooper, Lee A.D. Roth, Holger R. Xu, Daguang Bericat, David Floca, Ralf Zhou, S. Kevin Shuaib, Haris Farahani, Keyvan Maier-Hein, Klaus H. Aylward, Stephen Dogra, Prerna Ourselin, Sebastien Feng, Andrew School of Biomedical Engineering & Imaging Sciences King’s College London London United Kingdom NVIDIA Corporation Santa Clara and Bethesda United States School of Electrical and Computer Engineering Rice University Houston United States Department of Engineering Science University of Oxford Oxford United Kingdom Department of Medical Physics and Biomedical Engineering University College London London United Kingdom Department of BioAI InstaDeep Ltd London United Kingdom Department of Electrical & Computer Engineering University of Iowa Iowa City United States Mayo Clinic Jacksonville United States Mars Incorporated United States Div. Intelligent Medical Systems German Cancer Research Center Heidelberg Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany School of Biomedical Engineering Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Medical Physics Guy’s & St Thomas’ NHS Foundation Trust London United Kingdom National Cancer Institute Bethesda United States Kitware Inc. Clifton Park United States Department of Ophthalmology University of Colorado Aurora United States Frederick National Laboratory for Cancer Research Frederick United States Department of Pathology Northwestern University Chicago United States

Artificial Intelligence (AI) is having a tremendous impact across most areas of science. Applications of AI in healthcare have the potential to improve our ability to detect, diagnose, prognose, and intervene on human disease. For AI models to be used clinically, they need to be made safe, reproducible and robust, and the underlying software framework must be aware of the particularities (e.g. geometry, physiology, physics) of medical data being processed. This work introduces MONAI, a freely available, community-supported, and consortium-led PyTorch-based framework for deep learning in healthcare. MONAI extends PyTorch to support medical data, with a particular focus on imaging, and provide purpose-specific AI model architectures, transformations and utilities that streamline the development and deployment of medical AI models. MONAI follows best practices for software-development, providing an easy-to-use, robust, well-documented, and well-tested software framework. MONAI preserves the simple, additive, and compositional approach of its underlying PyTorch libraries. MONAI is being used by and receiving contributions from research, clinical and industrial teams from around the world, who are pursuing applications spanning nearly every aspect of healthcare. © 2022, CC BY.

关键词： Clinical research

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From Connectomic to Task-Evoked Fingerprints: Individualized Prediction of Task Contrasts from Resting-State Functional Connectivity 23rd

From Connectomic to Task-Evoked Fingerprints: Individualized...

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23rd International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2020

作者： Ngo, Gia H. Khosla, Meenakshi Jamison, Keith Kuceyeski, Amy Sabuncu, Mert R. School of Electrical and Computer Engineering Cornell University Ithaca United States Radiology Weill Cornell Medicine New York United States Brain and Mind Research Institute Weill Cornell Medicine New York United States Nancy E. and Peter C. Meinig School of Biomedical Engineering Cornell University Ithaca United States

ISBN: (纸本)9783030597276

Resting-state functional MRI (rsfMRI) yields functional connectomes that can serve as cognitive fingerprints of individuals. Connectomic fingerprints have proven useful in many machine learning tasks, such as predicting subject-specific behavioral traits or task-evoked activity. In this work, we propose a surface-based convolutional neural network (BrainSurfCNN) model to predict individual task contrasts from their resting-state fingerprints. We introduce a reconstructive-contrastive loss that enforces subject-specificity of model outputs while minimizing predictive error. The proposed approach significantly improves the accuracy of predicted contrasts over a well-established baseline. Furthermore, BrainSurfCNNs prediction also surpasses test-retest benchmark in a subject identification task. (Source code is available at https://***/ngohgia/brain-surf-cnn) © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Merlin: A Vision Language Foundation Model for 3D Computed Tomography

arXiv

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arXiv 2024年

作者： Blankemeier, Louis Cohen, Joseph Paul Kumar, Ashwin Van Veen, Dave Gardezi, Syed Jamal Safdar Paschali, Magdalini Chen, Zhihong Delbrouck, Jean-Benoit Reis, Eduardo Truyts, Cesar Bluethgen, Christian Jensen, Malte Engmann Kjeldskov Ostmeier, Sophie Varma, Maya Valanarasu, Jeya Maria Jose Fang, Zhongnan Huo, Zepeng Nabulsi, Zaid Ardila, Diego Weng, Wei-Hung Junior, Edson Amaro Ahuja, Neera Fries, Jason Shah, Nigam H. Johnston, Andrew Boutin, Robert D. Wentland, Andrew Langlotz, Curtis P. Hom, Jason Gatidis, Sergios Chaudhari, Akshay S. Department of Electrical Engineering Stanford University United States Stanford Center for Artificial Intelligence in Medicine and Imaging Stanford University United States Department of Radiology Stanford University United States Department of Radiology University of Wisconsin-Madison United States Department of Radiology Hospital Israelita Albert Einstein Brazil Department of Radiology University Hospital Zurich Switzerland Department of Computer Science Stanford University United States Department of Biomedical Data Science Stanford University United States Department of Medicine Stanford University United States

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs). However, current medical VLMs are generally limited to 2D images and short reports, and do not leverage electronic health record (EHR) data for supervision. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured EHR and unstructured radiology reports for supervision without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens). We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training dat

关键词： Semantics

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