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10th international Conference on Applied System Innovation, ICASI 2024

作者： Ahmad, Nisar Chen, Yao-Tien International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (纸本)9798350394924

medical image segmentation plays a crucial role in identifying and analyzing anatomical structures in medical images. This requires an accurate medical segmentation tool to delineate and quantitatively analyze the target regions, diagnose any abnormality, and assist in treatment planning. deep learning approaches have emerged as a promising solution for automating medical segmentation. However, challenges arise when dealing with the complex shapes and spatial variations of some target regions, especially in 3d MRI scans. To deal with such transformations, specific techniques are required to properly analyze and preprocess the dataset and perform image filtering to provide better features for improved prediction performance of deep learning architectures. This study focuses on improving brain tumor segmentation in multimodal 3d MRI images. We observed significant improvements in multimodal brain tumor segmentation results (accuracy, IoU, and mIoU) using an optimized 3d Gabor filter, which helps extract meaningful features. Multichannel input images were preprocessed to remove noise and select an appropriate resolution to reduce computational complexity. An improvement in mean Intersection over Union (mIoU) from 0.714 to 0.804 and accuracy from 0.982 to 0.991 were achieved, which shows a major improvement. This work contributes to the field of medical image segmentation by offering an improved and efficient approach for brain tumor analysis in 3d MRI scans, potentially aiding in diagnosis and treatment planning. © 2024 IEEE.

关键词： Magnetic resonance imaging

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Using electroencephalography signals to explore the influence of Zentangle on meditation and mindfulness 9

Using electroencephalography signals to explore the influenc...

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9th international Conference on Applied System Innovation, ICASI 2023

作者： Usman, Muhammad Lin, Chun-Ling Ming Chi University of Technology International Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices New Taipei City Taiwan Ming Chi University of Technology Department of Electrical Engineering New Taipei City Taiwan

ISBN: (纸本)9798350398380

Fifteen novices completed a Zentangle task while their brain activity was measured with electroencephalography (EEG) and their concentration emotional state, stress, and anxiety levels were evaluated with questionnaires. The study found increased activity in theta, alpha, beta, and gamma ranges associated with relaxation, focus, and mental activity. Participants reported improved concentration, emotional state, and reduced stress and anxiety levels after completing the task, suggesting the potential of Zentangle to enhance cognitive and emotional well-being. © 2023 IEEE.

关键词： Electroencephalography

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developing a Respiratory Pressure Simulation Platform for a Portable Negative Pressure Filtering Chest drainage System 16

Developing a Respiratory Pressure Simulation Platform for a ...

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16th IEEE international Conference on Nano/Molecular Medicine and engineering, NANOMEd 2023

作者： Liao, Hao-Yen Chu, Cheng-Shane Wu, Yang-Shun Lin, Sheng-Chieh Lin, Chia-Tsai Hung, Kuo-Yung Ming Chi University of Technology Dept. of Intl. Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices Taiwan Ming Chi University of Technology Department of Mechanical Engineering Taiwan Ming Chi University of Technology Research Center for Intelligent Medical Devices Taiwan

ISBN: (纸本)9798350343700

This study aims to reduce the risk of aerosol infection in patients and healthcare workers by establishing a portable negative pressure filtering chest drainage system (PNPFCdS) that filters aerosols and microparticles. By controlling the negative pressure precisely, the system can prevent aerosol transmission under different air leaks. By using a respiratory pressure simulation platform (RPSP), it has been demonstrated that the negative pressure filtering chest drainage system can achieve a minimum measured pressure error of 1.6% without simulated intrapleural pressure changes when the air leak flow rises to 800 ml/min. When the target pressure is -1.5 kPa under simulated intrapleural pressure changes with air leak of 500 ml/min, the measured pressure change rate ranges from -16.7 % to +22.7%. When the target pressure is -9 kPa, the measured pressure change rates ranges from -2% to +6.7%. According to ISO 10079-1, this study provides a model of a negative pressure filtering chest drainage system that is effective and safe. © 2023 IEEE.

关键词： Aerosols

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Eff-SAM: SAM-based Efficient Method for Brain Tumor Segmentation in Multimodal 3d MRI Scans

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IEEE Access 2025年 13卷 90063-90087页

作者： Ahmad, Nisar Chen, Yao-Tien Ming Chi University of Technology International Ph.D. Program in Innovative Technology of Biomedical Engineering Medical Devices New Taipei City243303 Taiwan

Accurate brain tumor segmentation in MRI scans is crucial for effective diagnosis and treatment planning, as even minor segmentation errors can lead to significant clinical concerns. However, this task is challenged by the complex anatomy of the brain, variable tumor shapes, and low contrast between tumor sub-regions. Moreover, existing methods often lack generalizability across diverse segmentation tasks and imaging modalities. Large Vision Model (LVM) like the Segment Anything Model (SAM) represents a foundational advancement in tumor segmentation. While SAM has shown impressive performance on natural images, its effectiveness in medical imaging, especially brain tumor segmentation, is limited due to domain differences and indistinct boundaries in MRI scans. To address these challenges, we propose Eff-SAM, a 3d brain tumor segmentation framework that adapts SAM for medical applications through Parameter-Efficient Fine-Tuning (PEFT) technique. The framework incorporates PEFT with adapters into SAM’s encoder to fine-tune the model effectively and optimize its performance for medical imaging while maintaining computational efficiency. Additionally, a Cross-Sliced Attention (CSA) mechanism captures semantic relationships, improves tumor localization, and is introduced into the encoder. Robust data preprocessing further enhances the model’s generalization across datasets. Eff-SAM demonstrates state-of-the-art performance and outperforms benchmark methods on the BraTS 2020 and BraTS 2021 datasets. It achieves dice scores of 0.884 for Whole Tumor (WT), 0.853 for Tumor Core (TC), and 0.818 for Enhancing Tumor (ET) on the BraTS 2020 dataset, and 0.880 (WT), 0.861 (TC), and 0.821 (ET) on the BraTS 2021 dataset. This work highlights the potential of integrating vision models like SAM with lightweight, domain-specific modules to deliver accurate and efficient brain tumor segmentation offering a clinically valuable tool, especially in scenarios with limited data and complex tu

关键词： Semantic Segmentation

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TiO2 Hole Transport Layer Incorporated in a Thermally Evaporated Sb2Se3 photoelectrode Exhibiting Low Onset Potential for photoelectrochemical Applications

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Energy and Fuels 2024年第17期38卷 16936-16948页

作者： Xavier, Thatheyus Peter Piraviperumal, Malar Kuo, Chih-Yu Govindasamy, Mani Thinfilm Photovoltaics Laboratory Department of Physics and Nanotechnology SRM Institute of Science and Technology Tamil Nadu Kattankulathur603203 India Department of Chemical Engineering & Biotechnology National Taipei University of Technology Taipei City10608 Taiwan Research Center for Intelligence Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai602105 India Taipei10608 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City243303 Taiwan

developing cost-effective photoelectrodes with a low band gap in the NIR-visible regions remains a challenge to achieve effective hydrogen production. We report the details of superstrate-configured photocathodes, consisting of antimony selenide (Sb2Se3) light absorbing layers with titanium dioxide (TiO2) as a hole transport layer, exhibiting the capability for hydrogen evolution with broad range light absorption enabled by the low band gap value of ∼1.2 eV of Sb2Se3. Strong anisotropy is a significant characteristic property of Sb2Se3, allowing superior electrical transportation along the [hkl] crystallographic orientation, which is obtained via close space evaporation of presynthesized bulk material at a growth temperature of 250 °C, over the solution-processed TiO2 film on a fluorine-doped tin oxide (FTO) substrate. The superstrate-configured FTO/TiO2/Sb2Se3 photocathode exhibited a photocurrent density of 100 μA cm-2 at −0.3 V vs RHE in 0.5 M H2SO4 under 1 sun illumination. To further improve photocurrent density, Pt as a cocatalyst was deposited over Sb2Se3 that led to enhanced photocurrent to 3 mA cm-2 at −0.3 V vs RHE in 0.5 M H2SO4 under 1 sun illumination. despite the relatively lower photocurrent observed, which resulted due to a large conduction band offset (ΔECB = 0.99 eV) of the TiO2/Sb2Se3 interface in a superstrate configuration, a significantly lower onset potential of −0.1 V vs RHE was observed. The results are encouraging due to the low band gap of Sb2Se3, photocorrosion stability, and straightforward fabrication, which is expected to assist for significant outcomes. © 2024 American Chemical Society.

关键词： Titanium dioxide

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3d Brain Tumor Segmentation in Multimodal MRI Images

3D Brain Tumor Segmentation in Multimodal MRI Images

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IEEE international Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

In brain tumor research, the use of machine learning based methods using MRI data to locate brain tumors accurately has significantly increased and new techniques are proposed to facilitate the medical experts. There is still room for improvement as tumors have complex anatomy which requires efforts to explore further and propose new deep learning based solutions. In this research, we propose 3d UNet based multimodal brain tumor segmentation for Non Enhancing Tumor (NET), Peritumoral Edema (PE), and Enhancing Tumor (ET). data preprocessing and augmentation techniques are employed to optimise the model's performance. The dataset used in this study to train, validate, and test the model performance is the BraTS 2020 dataset. The model achieved improved accuracy and IoU after implementing the mentioned techniques. Results before and after data augmentation demonstrate a significant enhancement in model performance. The model achieved an improved mIoU of 0.77 and accuracies of 98.21% and 99.36% for actual data and augmented data for three brain tumor classes, i.e., NET, PE, and ET. Our model performed multiclass brain tumor segmentation with high accuracies, and we aim to enhance this study to propose a valuable medical assistance model.

关键词： Solid modeling Image segmentation Three-dimensional displays Accuracy Magnetic resonance imaging Brain modeling data augmentation

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Enhanced deep Learning Model Performance in 3d Multimodal Brain Tumor Segmentation with Gabor Filter

Enhanced Deep Learning Model Performance in 3D Multimodal Br...

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international Conference on Applied System Innovation (ICASI)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

ISBN: (数字)9798350394924

ISBN: (纸本)9798350394931

关键词： deep learning Image segmentation Solid modeling Three-dimensional displays Magnetic resonance imaging Feature extraction Planning

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Eosin-Y containing electrospun fibers for optical ammonia sensing based on wavelength shift 8

Eosin-Y containing electrospun fibers for optical ammonia se...

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8th international Conference of Precision Machinery and Manufacturing technology, ICPMMT 2023

作者： Simanjuntak, M.S. Chu, C.S. Rispandi, R. Putro, d. Department of Mechanical Engineering Ming Chi University of Technology New Taipei City24301 Taiwan Research Center for Intelligent Medical Devices Ming Chi University of Technology New Taipei City24301 Taiwan International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City24301 Taiwan

Sensors in recent days have been in great demand in industrial zones of simple, low-cost sensors for effectively sensing various gases. This research work develops electrospinning fibers for optical ammonia sensing based on the wavelength shift. The optical sensing fibers were produced approach by the electrospinning technique. The addition of cellulose acetate (CA) matrix with doped Eosin-Y is an NH3 fluorophore. The same light emitting diode (LEd) light source was used to excite NH3-sensitive dyes at 380 nm peak wavelength. display the eosin Y emission spectrum, with a maximum wavelength of 582 nanometers. The observed red emission wavelength shift and fluorescence intensity at 582 nm decreased with increasing concentrations of NH3. The emission spectrum of eosin-Y fluorescence at 582 nanometers is changed with increasing NH3 concentrations ranging from 0 to 500 parts per million. In this work, according to the experiment results, the sensitivity of NH3 sensors is 7.73. Finally, the electrospinning fibers for optical NH3 sensing developed as a result of this research enable effective sensing of NH3 concentrations in practical applications in a variety of fields, including medical and industrial. © Published under licence by IOP Publishing Ltd.

关键词： Light sources

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UNet-based Automatic Multicalss Brain Tissue Segmentation

UNet-based Automatic Multicalss Brain Tissue Segmentation

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IEEE international Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Nisar Ahmad Yao-Tien Chen International Ph.D. Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology New Taipei City Taiwan

In brain research, brain tissue segmentation techniques have offered vast aid and possibilities for quantitative analysis of the brain to detect brain tumor and other brain issues. In this paper, UNet-based multiclass brain tissue segmentation is presented. To achieve the best performance of the model, data augmentation techniques are applied. The training, validation and testing of the model is performed on the BrainWeb dataset. The model has achieved improved accuracy and IoU after data augmentation. Both pre and post data augmentation results are presented which show clear improvement in model performance after data augmentation. The achieved IoU for white matter, gray matter and cerebrospinal fluid are 0.84, 0.95 and 0.92, respectively. Our model accurately performed the multiclass brain tissue segmentation and this study proposes a valuable medical aid model.

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developing a Respiratory Pressure Simulation Platform for a Portable Negative Pressure Filtering Chest drainage System

Developing a Respiratory Pressure Simulation Platform for a ...

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IEEE international Conference on Nano/Molecular Medicine and engineering (NANOMEd)

作者： Hao-Yen Liao Cheng-Shane Chu Yang-Shun Wu Sheng-Chieh Lin Chia-Tsai Lin Kuo-Yung Hung Department of International Ph.D Program in Innovative Technology of Biomedical Engineering and Medical Devices Ming Chi University of Technology TAIWAN Department of Mechanical Engineering Ming Chi University of Technology TAIWAN Research Center for Intelligent Medical Devices Ming Chi University of Technology TAIWAN

This study aims to reduce the risk of aerosol infection in patients and healthcare workers by establishing a portable negative pressure filtering chest drainage system (PNPFCdS) that filters aerosols and microparticles. By controlling the negative pressure precisely, the system can prevent aerosol transmission under different air leaks. By using a respiratory pressure simulation platform (RPSP), it has been demonstrated that the negative pressure filtering chest drainage system can achieve a minimum measured pressure error of 1.6% without simulated intrapleural pressure changes when the air leak flow rises to 800 ml/min. When the target pressure is −1.5 kPa under simulated intrapleural pressure changes with air leak of 500 ml/min, the measured pressure change rate ranges from −16.7 % to +22.7%. When the target pressure is −9 kPa, the measured pressure change rates ranges from −2% to +6.7%. According to ISO 10079-1, this study provides a model of a negative pressure filtering chest drainage system that is effective and safe.

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