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Iranian Conference of biomedical Engineering (ICBME)

作者： Mohammad Almasganj Emad Fatemizadeh Department of Electrical Engineering Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

Retinal optical coherence tomography (OCT) images are widely used to diagnose and grade macular diseases, such as age-related macular degeneration (AMD). However, manual interpretation of OCT images is time-consuming and subjective. Therefore, automated and accurate classification of OCT images is essential for assisting ophthalmologists in clinical decision-making. This paper proposes a pyramidal deep neural network that can diagnose normal and two types of AMD (dry and wet) in OCT images. Our network leverages features from different scales of a pre-trained convolutional neural network (CNN) and integrates them with two advanced versions of feature pyramid networks: bidirectional feature pyramid network (BiFPN) and path aggregation network (PANet). We evaluate our network on the NEH dataset and compare it with its predecessor. Our results show that our BiFPN-VGG16 and PAN-VGG16 models achieve accuracies of 94.S% and 95.0%, respectively, which are 2.8 to 3% higher than the previous models. Our approach demonstrates the potential of multi-scale feature networks for OCT image classification and can serve as an auxiliary diagnostic tool for ophthalmologists.

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Development of an Intelligent System for Detection of Chronic Stress from Biological signal processing 9th

Development of an Intelligent System for Detection of Chroni...

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9th Latin American Congress on biomedical Engineering, CLAIB 2022 and 28th Brazilian Congress on biomedical Engineering, CBEB 2022

作者： Junqueira, Luis Pina, Marta Biomedical Engineering Laboratory of Signal Processing and Medical Image SP São Paulo Brazil Federal Institute of Education Science and Technology IFSP SP São Paulo Brazil

ISBN: (纸本)9783031494031

Our objective in this work was to develop an intelligent system able to perform an automated detection of chronic stress, based on biological signals processing and features extraction, confronted with Hans Selye clinical model of stress phase’s assessment. We recorded biological signals of blood pressure, skin surface temperature, galvanic skin resistance and heart rate of 120 health adult volunteers. Also the heart rate variability parameters were extracted for the time-domain and frequency-domain analysis. A Multi-Layer Perceptron Artificial Neural Network with a supervised learning approach was applied to generate the mathematical model. The system classification produced a precision index of 89% for identifying the stressed class, when applying the skin temperature and the heart rate variability parameters as input of the neural network, providing a satisfactory initial performance in the discrimination of stressed individuals. A worth training set with more examples mighty potentially increases the precision for identifying the stressed individuals. Despite study limitations, we consider that the use of intelligent systems to classify biological signals and identify the long term stress presence in human organism could contribute to a more objective analysis of such physiologic characteristics associated to the chronic stress and its implications in human health and performance. Future works should include different settle of physiological parameters and different machine learning techniques for analysis and classification of signals. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Intelligent systems

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Bacterial community in response to packaging conditions in farmed gilthead seabream

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Aquaculture and Fisheries 2023年第4期8卷 410-421页

作者： Apollon Thomas Spyros J.Konteles Sotiris Ouzounis Spyros Papatheodorou Aliki Tsakni Dimitra Houhoula Theofania Tsironi Department of Food Science and Technology Faculty of Food SciencesUniversity of West AtticaAthens11521Greece Medical Image and Signal Processing Laboratory Department of Biomedical EngineeringUniversity of West AtticaAthens11521Greece Laboratory of Food Process Engineering Department of Food Science and Human NutritionAgricultural University of AthensAthens11855Greece

The objective of this study was to evaluate the effect of modified atmosphere packaging(MAP)on fish skin,gills and intestines bacterial *** gilthead seabream was packed aerobically or under modified atmospheres(60%CO_(2),30%N_(2),10%O_(2))and stored isothermally at 0℃.Next Generation Sequencing(NGS)analysis was applied for the characterization of fish microbiome on fish skin,gills,and intestines initially(time of packaging,1 day after harvesting)and after 10 days of isothermal storage at 0℃.NGS results indicated statistically significant differences in families’richness and diversity in the tested fish tissues between aerobic and MAP packaging during storage at 0℃.The most persistent bacteria were Proteobacteria for both packaging *** fish skin microbiota,the initially prevailing families were Comamonadaceae,Enterobacteriaceae and Moraxellaceae while in the intestines Comamonadaceae,Anaplasmataceae,Bacillaceae and Enterobacteriaceae were the dominant *** the end of storage period,the fish microbiota was dominated by psychotropic and psychrophilic families(Pseudoalteromonadaceae,Psychromonadaceae,and Shewanellaceae),while families such as Comamonadaceae were persistent under MAP *** 8 days of fish storage at 0℃,МАРsamples exhibited higher sensory scorings than the respective aerobically stored fish,indicating better retention of fish freshness and fish quality attributes under *** on the results of the study,MAP modified significantly the microbiological status and extended the shelf life of *** was a powerful tool that provided a more complete assessment compared to a culture-based analysis.

关键词： Fish Preservation Next generation sequencing Modified atmosphere packaging Microbiome

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Anomaly Detection in Brain Tumor Imaging: Few-Shot Learning with Generative Models and Knowledge Transfer

Anomaly Detection in Brain Tumor Imaging: Few-Shot Learning ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： A. Khodabakhsh A. Aghaee N. Niroomand H. Arabi H. Zaidi School of Electrical Engineering Sharif University of Technology Biomedical Signal and Image Processing Laboratory (BiSIPL) Tehran Islamic Republic of Iran School of Electrical and Computer Engineering Toronto Metropolitan University Car-diovascular Imaging Modeling and Biomechanics Group Toronto Ontario Canada Division of Nuclear Medicine & Molecular Imaging Genève Geneva University Hospital Genève Switzerland

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Anomaly detection plays a vital role in the early identification of brain tumors in MRI scans, and it directly impacts diagnostic accuracy and patient outcomes. Despite its importance, current methods often fall short in handling sparse labeled data and precisely localizing anomalies. In this study, an innovative method that integrates Generative Adversarial Networks (GANs) with few-shot learning and transfer learning techniques, offering a new perspective in handling scarce labeled data in medical image analysis. At the core of the proposed method is a Vision Transformer-based generator, showcasing the dedication to advancing medical diagnostics technology. This generator, paired with a uniquely adapted discriminator benefiting from a pre-trained VGG16 network, enhances the model’s efficiency and accuracy in anomaly detection. The efficacy of the proposed method is demonstrated through its ability to distinguish between normal and patho- logical brain images obtained from a public dataset. In our study, the model achieved anomaly scores of approximately $0.14 \pm 0.18$ for normal images and $0.23 \pm 0.76$ for abnormal images. The enhanced precision achieved in anomaly detection and localization signifies a notable advancement beyond current methodologies. These outcomes pave the way for the creation of increasingly sophisticated and dependable diagnostic instruments, thereby facilitating more precise detection of brain tumors.

关键词： Adaptation models Accuracy Transfer learning Transformer cores Brain modeling Transformers Generators

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Attention-based deep learning segmentation: Application to brain tumor delineation

Attention-based deep learning segmentation: Application to b...

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Iranian Conference of biomedical Engineering (ICBME)

作者： Reza Karimzadeh Emad Fatemizadeh Hossein Arabi Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva Switzerland

ISBN: (纸本)9781665499859

Brain tumor segmentation is an important step in brain cancer diagnosis and treatment. Manual segmentation is highly time consuming and tedious. To address these issues deep learning-base methods have been employed and demonstrated tremendous improvement in terms of performance accuracy and time-efficiency compared to the conventional segmentation methods. Due to varying size, location and shape of tumors, tumor segmentation task is highly challenging in comparison to the anatomical organ segmentation, thus it seems essential to develop robust, accurate, and efficient deep learning-based solutions for this task. In this work, we proposed a method that utilized UN et architecture as backbone (referred to as Attention-based UNet (AbUNet)) to establish an efficient tumor segmentation framework. AbUNet consist of two basic components, attention and segmentation modules. The purpose of attention module is to estimate approximate location of tumor through a rough/coarse segmentation with high true positive rate to guarantee the inclusion of the entire tumor. To this end, a new cost function was introduced and the dilated masks of ground-truths were employed for training of the attention module. The outcome and some feature maps from decoder part of attention module were fed to the segmentation module to guide the segmentation network focusing on the confined regions of the image which contain tumor. In the last step, the segmentation module predicts final mask of the tumor. To evaluate our proposed method, we used the BraTS dataset which contains brain MRI scans with manually defined tumor masks delineated by an expert. Our baseline UNet without attention module achieved a Dice score of 0.68 compared to AbUNet with Dice score of 0.79 that indicates significant improvement in segmentation accuracy. It was demonstrated that incorporation of the proposed attention module in the deep learning-based segmentation network would significantly enhance the robustness and accuracy o

关键词： Training image segmentation Shape Magnetic resonance imaging Focusing Manuals Robustness

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Implementation of a Biometric Interface in Voice Controlled Wheelchairs

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Sound & Vibration 2020年第1期54卷 1-15页

作者： Lamia Bouafif Noureddine Ellouze National Institute of Biomedical Studies of Tunis 1092TunisTunisia Image and Signal Processing Laboratory ENIT BP 37University of Tunis El Manar1064Tunisia

In order to assist physically handicapped persons in their movements,we developed an embedded isolated word speech recognition system(ASR)applied to voice control of smart ***,in spite of the existence in the industrial market of several kinds of electric wheelchairs,the problem remains the need to manually control this device by hand via joystick;which limits their use especially by people with severe ***,a significant number of disabled people cannot use a standard electric wheelchair or drive it with *** proposed solution is to use the voice to control and drive the wheelchair instead of classical *** intelligent chair is equipped with an obstacle detection system consisting of ultrasonic sensors,a moving navigation algorithm and a speech acquisition and recognition module for voice control embedded in a DSP *** ASR architecture consists of two main *** first one is the speech parameterization module(features extraction)and the second module is the classifier which identifies the speech and generates the control word to motors power *** training and recognition phases are based on Hidden Markov Models(HMM),K-means,Baum-Welch and Viterbi *** database consists of 39 isolated speaker words(13 words pronounced 3 times under different environments and conditions).The simulations are tested under Matlab environment and the real-time implementation is performed by C language with code composer studio embedded in a TMS 320 C6416 DSP *** results and experiments obtained gave promising recognition ratio and accuracy around 99%in clean ***,the system accuracy decreases considerably in noisy environments,especially for SNR values below 5 dB(in street:78%,in factory:52%).

关键词： Handicapped persons smart wheelchairs voice control speech recognition training and classification

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U-Net Based Estimation of Functional Connectivity from Time Series Multi-Channel EEG from Schizophrenia Patients

U-Net Based Estimation of Functional Connectivity from Time ...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： Alireza Khodabakhsh Hossein Arabi Habib Zaidi Biomedical Signal and Image Processing Laboratory (BiSIPL) School of Electrical Engineering Sharif University of Technology Tehran Iran Division of Nuclear Medicine & Molecular Imaging Geneva University Hospital Geneva Switzerland

ISBN: (数字)9781665421133

ISBN: (纸本)9781665421140

Human brain, as a complex network, is affected by many mental disorders and neurodegenerative diseases. Brain functional and structural alteration could be captured by imaging modalities such as CT and MR imaging. However, these modalities have low sensitivity to properly capture the brain connectivity map as a biomarker for the early diagnosis of neurodegenerative diseases. In this light, complimentary examination tools (such as EEG) are employed to estimate the brain functional connectivity (FC) map. To decode the brain FC map from EEG signals, conventional approaches rely on hand-craft feature extraction, leading to suboptimal performance/effectiveness. In this light, this work set out to implement a novel deep neural network based on U-Net to extract the brain FC maps and identify (based on the obtained FC map) the type of neurodegenerative disease from the patient’s EEG signals. Due to the absence of the ground truth brain FC maps, the proposed approach extracts the patient-specific brain FC maps in an unsupervised approach. To evaluate the performance of the proposed deep learning model, a publicly available dataset of EEG signals from healthy control and schizophrenia patients was employed. The proposed model exhibited an accuracy of 94.11% to classify schizophrenia patients. Moreover, the estimated brain FC maps for both healthy control and schizophrenia patients exhibited highly discriminative patterns to differentiate abnormalities from the healthy controls. The proposed unsupervised model, which is applicable to EEG and functional MR data, exhibited promising performance to extract brain FC maps and classify neurodegenerative diseases.

关键词： Deep learning Sensitivity Mental disorders Time series analysis Brain modeling Feature extraction Electroencephalography

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3D dilated and residual convolutional neural network for COVID-19 detection from the chest computed tomography

3D dilated and residual convolutional neural network for COV...

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Iranian Conference of biomedical Engineering (ICBME)

作者： Reza Karimzadeh Nona Rajabi Emad Fatemizadeh Hossein Arabi Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran Devision of Biomedical Engineering Sharif University of Technology Tehran Iran Division of Nuclear Medicine and Molecular Imaging Geneva University Hospital Geneva Switzerland

ISBN: (纸本)9781665499859

Chest Computed Tomography (CT) is regarded as one of the most effective tools in diagnosing COVID-19 due to its high sensitivity and ease of use. However, analysis of CT images may be time-consuming for the clinicians, which highly influence their performance. Artificial-intelligence-based methods can help automating the process of interpreting chest CT images and diagnosis of COVID-19 in suspicious patients. In this paper, we propose a 3D deep convolutional neural network for classifying chest CT images into COVID-19-infected and normal classes. Dilated convolution and residual connections are employed to increase the model's performance by enlarging the receptive field of the kernels and direct propagation of the information. The accuracy, precision, sensitivity, specificity, and F1-score achieved by our model are 0.99, 0.98, 1.0, 0.979, and 0.99, respectively. The high sensitivity value of the model demonstrates its efficiency in detecting/identifying all the infected patients correctly, which allows early quarantine and the start of the treatment process.

关键词： COVID-19 Solid modeling Sensitivity Three-dimensional displays image resolution Convolution Computed tomography

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Effects of spatially dense adrenergic stimulation to rotor behaviour in simulated atrial sheets

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Computers in Biology and Medicine 2024年 182卷 109195-109195页

作者： Magtibay, Karl Massé, Stéphane Nanthakumar, Kumaraswamy Umapathy, Karthikeyan Biomedical Signal and Image Processing Laboratory Department of Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science Toronto Metropolitan University 350 Victoria St TorontoONM5B 2K3 Canada Toby Hull Cardiac Fibrillation Management Toronto General Hospital University Health Network 200 Elizabeth Street TorontoONM5G 2C4 Canada

Sympathetic hyperactivity via spatially dense adrenergic stimulation may create pro-arrhythmic substrates even without structural remodelling. However, the effect of sympathetic hyperactivity on arrhythmic activity, such as rotors, is unknown. Using simulations, we examined the effects of gradually increasing the spatial density of adrenergic stimulation (AS) in atrial sheets on rotors. We compared their characteristics against rotors hosted in atrial sheets with increasing spatial density of minimally conductive (MC) elements to simulate structural remodelling due to injury or disease. We generated rotors using an S1-S2 stimulation protocol. Then, we created phase maps to identify phase singularities and map their trajectory over time. We measured each rotor's duration (s), angular speed (rad/s), and spatiotemporal organization. We demonstrated that atrial sheets with increased AS spatial densities could maintain rotors longer than with MC elements (2.6 ± 0.1 s vs. 1.5 ± 0.2 s, p © 2024 The Authors

关键词： Digital elevation model

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Robot Control Using an Inexpensive P300 Based BCI

Robot Control Using an Inexpensive P300 Based BCI

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Iranian Conference of biomedical Engineering (ICBME)

作者： Shafie Bahman Mohammad B. Shamsollahi Biomedical Signal and Image Processing Laboratory (BiSIPL) Sharif University of Technology Tehran Iran

ISBN: (数字)9781728156637

ISBN: (纸本)9781728156644

Brain Computer Interfaces (BCI) are an important concept of biomedical engineering because of their ability to improve life conditions for people with different disabilities. Lots of studies have worked on important characteristics of BCI systems, such as speed and accuracy, whereas price is an important aspect too. Thus, a low cost BCI system with high accuracy clearly can help more people. Our purpose in this study is to design a P300 based BCI system using a low-priced EEG headset which has an acceptable accuracy. Our final design got a mean real-time accuracy of 93.3% which is comparable to systems with much more expensive hardware.

关键词： Electroencephalography Robots Real-time systems Task analysis Graphical user interfaces Mathematical model biomedical engineering

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