Piyawattanametha, Wibool

Preferred name

Main Affiliation

School of Engineering

wibool.pi@kmitl.ac.th

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Monocular depth estimation based on deep learning for intraoperative guidance using surface-enhanced Raman scattering imaging
(2025-02-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
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Yang, Chia Wei
Imaging of surface-enhanced Raman scattering (SERS) nanoparticles (NPs) has been intensively studied for cancer detection due to its high sensitivity, unconstrained low signal-to-noise ratios, and multiplexing detection capability. Furthermore, conjugating SERS NPs with various biomarkers is straightforward, resulting in numerous successful studies on cancer detection and diagnosis. However, Raman spectroscopy only provides spectral data from an imaging area without co-registered anatomic context. This is not practical and suitable for clinical applications. Here, we propose a custom-made Raman spectrometer with computer-vision-based positional tracking and monocular depth estimation using deep learning (DL) for the visualization of 2D and 3D SERS NPs imaging, respectively. In addition, the SERS NPs used in this study (hyaluronic acid-conjugated SERS NPs) showed clear tumor targeting capabilities (target CD44 typically overexpressed in tumors) by an ex vivo experiment and immunohistochemistry. The combination of Raman spectroscopy, image processing, and SERS molecular imaging, therefore, offers a robust and feasible potential for clinical applications.
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Isolation of circulating tumor cells under hydrodynamic loading using microuidic technology
(2014-01-01)
Zhao, Cong
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Lee, Yi Kuen
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Xu, Rui
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Liang, Chun
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Liu, Dayu
Cancer is a leading cause of mortality worldwide causing human deaths. Circulating tumor cells (CTCs) are cells that have detached from a primary tumor and circulate in the bloodstream; they may constitute seeds for subsequent growth of additional tumors (metastasis) in different tissues. The detection of CTCs may have important prognostic and therapeutic implications but, because their number is very small, these cells are not easily detected. Circulating tumor cells are found in the order of 10-100 CTCs per mL of whole blood in patients with metastatic disease. Isolation of tumor cells circulating in the blood stream, by immobilizing them on surfaces functionalized with bio-active coating within microfluidic devices, presents an interdisciplinary challenge requiring expertise in different research areas: cell biology, surface chemistry, fluid mechanics and microsystem technology. We first review the fundamental of cell biology of CTCs and summarize the key microfluidic techniques for isolation of CTCs via cell-ligand interactions, magnetic interactions, filtration; detection and enumeration of CTCs; in vivo CTCs imaging.
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Picomolar Dopamine Detection using Colloidal Surface-Enhanced Raman Scattering Based on Benzene-Dithiol–Linked Gold Nanoparticles in Solution
(2026-04-01)
Sucharitakul, Waraporn
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Danvirutai, Pobporn
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Pinlaor, Somchai
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Piyawattanametha, Wibool
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Srichan, Chavis
Sensitive detection of dopamine at ultralow concentrations is essential for the development of advanced chemical and neurochemical sensing technologies. Surface-enhanced Raman scattering (SERS) offers molecular specificity and high sensitivity, but its analytical reliability is often limited by poor signal reproducibility arising from uncontrolled nanoparticle aggregation. In this work, we report a solvent-based colloidal SERS platform for picomolar dopamine detection using benzene-1,4-dithiol (BDT)–linked gold nanoparticles (AuNPs). BDT serves as a molecular linker that defines sub-nanometer plasmonic nanogaps and simultaneously acts as an intrinsic Raman reference. Dopamine molecules introduced in dilute solution are efficiently sampled within these nanogaps through noncovalent interactions, resulting in strong and reproducible SERS enhancement. The proposed platform demonstrates a detection limit in the picomolar range, a wide linear dynamic response, and excellent signal stability without the use of biological or artificial matrices. This study presents a solvent-based SERS strategy for quantitative liquid-phase dopamine detection in aqueous environments, offering a foundation for further development in sensor calibration and integration with biological matrices.
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2-D MEMS scanner for handheld multispectral Dual-Axis confocal microscopes
(2018-08-01)
Jung, Il Woong
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Lopez, Daniel
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Qiu, Zhen
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Piyawattanametha, Wibool
We describe a 2-D microelectromechanical systems (MEMS) scanner for a handheld multispectral confocal microscope for early detection and diagnosis of cervical cancer. This paper is an expansion of work originally reported in the Proceedings of IEEE International Conference on Optical MEMS and Nanophotonics 2012. The MEMS scanner and fabrication process has been designed to improve performance for the dual-axis confocal microscopy architecture. Dual-axis confocal microscopy achieves comparable transverse and depth resolution to high numerical aperture (NA) single-axis confocal microscopy but achieves longer working distance by utilizing low-NA optics. The MEMS scanner has an inner gimbal design with torsional flexures separated from the reflectors to reduce light loss and oxide-free outer axis torsional flexures by utilizing poly vias for electrical access to the inner gimbal electrodes. The devices are large-scale batch fabricated using a double layer silicon-on-insulator (SOI) process and predice postreleasing of dies. The scanner has electrostatic optical deflection angles of 2° for the single-sided inner axis at 75 V and ±3° for the outer axis at 120 V. The device has resonance frequencies of ∼2.48 kHz and ∼348 Hz for the inner and outer axis torsional modes, respectively. Reflectance and fluorescence images with 650μm × 800μm field of view are demonstrated at 15 frames/s. The transverse (axial) resolutions are 4.78μm ( 6.2μm ), 5.77μm ( 6.2μm ), and 7.45μm ( 5.6μm ) for wavelengths of 561, 662, and 781 nm, respectively.
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Bioengineering horizon scan 2020
(2020-05-01)
Kemp, Luke
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Adam, Laura
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Boehm, Christian R.
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Breitling, Rainer
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Casagrande, Rocco
Horizon scanning is intended to identify the opportunities and threats associated with technological, regulatory and social change. In 2017 some of the present authors conducted a horizon scan for bioengineering (Wintle et al., 2017). Here we report the results of a new horizon scan that is based on inputs from a larger and more international group of 38 participants. The final list of 20 issues includes topics spanning from the political (the regulation of genomic data, increased philanthropic funding and malicious uses of neurochemicals) to the environmental (crops for changing climates and agricultural gene drives). The early identification of such issues is relevant to researchers, policy-makers and the wider public.
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Super-resolution and segmentation deep learning for breast cancer histopathology image analysis
(2023-01-01)
Juhong, Aniwat
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Li, Bo
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Yao, Cheng You
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Yang, Chia Wei
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Agnew, Dalen W.
Traditionally, a high-performance microscope with a large numerical aperture is required to acquire high-resolution images. However, the images’ size is typically tremendous. Therefore, they are not conveniently managed and transferred across a computer network or stored in a limited computer storage system. As a result, image compression is commonly used to reduce image size resulting in poor image resolution. Here, we demonstrate custom convolution neural networks (CNNs) for both super-resolution image enhancement from low-resolution images and characterization of both cells and nuclei from hematoxylin and eosin (H&E) stained breast cancer histopathological images by using a combination of generator and discriminator networks so-called super-resolution generative adversarial network-based on aggregated residual transformation (SRGAN-ResNeXt) to facilitate cancer diagnosis in low resource settings. The results provide high enhancement in image quality where the peak signal-to-noise ratio and structural similarity of our network results are over 30 dB and 0.93, respectively. The derived performance is superior to the results obtained from both the bicubic interpolation and the well-known SRGAN deep-learning methods. In addition, another custom CNN is used to perform image segmentation from the generated high-resolution breast cancer images derived with our model with an average Intersection over Union of 0.869 and an average dice similarity coefficient of 0.893 for the H&E image segmentation results. Finally, we propose the jointly trained SRGAN-ResNeXt and Inception U-net Models, which applied the weights from the individually trained SRGAN-ResNeXt and inception U-net models as the pre-trained weights for transfer learning. The jointly trained model’s results are progressively improved and promising. We anticipate these custom CNNs can help resolve the inaccessibility of advanced microscopes or whole slide imaging (WSI) systems to acquire high-resolution images from low-performance microscopes located in remote-constraint settings.
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Theoretical modeling and experimental investigation of in-phase resonant MEMS mirrors with cascaded structures
(2024-05-01)
Chen, Wenhao
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Luo, Huahuang
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Tavakkoli, Hadi
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Duan, Mingzheng
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Piyawattanametha, Wibool
This paper proposes an efficient nonlinear one-dimensional (1D) compact mass-damper-spring model to predict the dynamic response of electrostatic resonant micro-electro-mechanical system (MEMS) mirrors with cascaded structures. The time-dependent damping moment due to viscous shear and pressure drag is computed using semi-empirical analytical equations for comb-drive structures and device frames. Nonlinear electrostatic force induced by the comb drives is efficiently acquired based on the hybrid method. The optimized device is fabricated using MEMS fabrication processes based on a 4-inch silicon-on-insulator wafer. The proposed compact model with the measured key parameters from the fabricated device shows excellent capability to accurately predict nonlinear dynamic responses of the fabricated device, including parametric excitation and hysteretic frequency response, with an average error of less than 5%. In particular, our 1D model is three orders of magnitude faster than the conventional finite element method model (0.8 s versus 1 h), enabling efficient system-level optimization of the critical design parameters. Based on the parametric study, electrode gap distance and torsion spring width are found to be two critical design parameters and dimensional analysis is conducted for design optimization with scan angle enhancing from 16.8° to 24° compared with the first design.
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Recurrent and convolutional neural networks for sequential multispectral optoacoustic tomography (MSOT) imaging
(2023-11-01)
Juhong, Aniwat
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Li, Bo
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Liu, Yifan
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Yao, Cheng You
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Yang, Chia Wei
Multispectral optoacoustic tomography (MSOT) is a beneficial technique for diagnosing and analyzing biological samples since it provides meticulous details in anatomy and physiology. However, acquiring high through-plane resolution volumetric MSOT is time-consuming. Here, we propose a deep learning model based on hybrid recurrent and convolutional neural networks to generate sequential cross-sectional images for an MSOT system. This system provides three modalities (MSOT, ultrasound, and optoacoustic imaging of a specific exogenous contrast agent) in a single scan. This study used ICG-conjugated nanoworms particles (NWs-ICG) as the contrast agent. Instead of acquiring seven images with a step size of 0.1 mm, we can receive two images with a step size of 0.6 mm as input for the proposed deep learning model. The deep learning model can generate five other images with a step size of 0.1 mm between these two input images meaning we can reduce acquisition time by approximately 71%.
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Novel p16 binding peptide development for p16-overexpressing cancer cell detection using phage display
(2015-01-01)
Khemthongcharoen, Numfon
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Ruangpracha, Athisake
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Sarapukdee, Pongsak
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Rattanavarin, Santi
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Jolivot, Romuald
Protein p<sup>16INK4a</sup> (p16) is a well-known biomarker for diagnosis of human papillomavirus (HPV) related cancers. In this work, we identify novel p16 binding peptides by using phage display selection method. A random heptamer phage display library was screened on purified recombinant p16 protein-coated plates to elute only the bound phages from p16 surfaces. Binding affinity of the bound phages was compared with each other by enzyme-linked immunosorbent assay (ELISA), fluorescence imaging technique, and bioinformatic computations. Binding specificity and binding selectivity of the best candidate phage-displayed p16 binding peptide were evaluated by peptide blocking experiment in competition with p16 monoclonal antibody and fluorescence imaging technique, respectively. Five candidate phage-displayed peptides were isolated from the phage display selection method. All candidate p16 binding phages show better binding affinity than wild-type phage in ELISA test, but only three of them can discriminate p16-overexpressing cancer cell, CaSki, from normal uterine fibroblast cell, HUF, with relative fluorescence intensities from 2.6 to 4.2-fold greater than those of wild-type phage. Bioinformatic results indicate that peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' binds to p16 molecule with the best binding score and does not interfere with the common protein functions of p16. Peptide blocking experiment shows that the phage-displayed peptide 'Ser-His-Ser-Leu-Leu-Ser-Ser' can conceal p16 from monoclonal antibody interaction. This phage clone also selectively interacts with the p16 positive cell lines, and thus, it can be applied for p16-overexpressing cell detection.
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Development of a Telehealth-Enabled Portable Optical Endomicroscopy System with Targeted Peptides: A Preclinical Feasibility Study for Cervical Cancer Detection
(2026-04-01)
Thaijiam, Chanchai
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Navaitthiporn, Nitipon
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Rithcharung, Preeyarat
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Piyawattanametha, Nicholas
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Komai, Shoji
Background/Objectives: We developed a telehealth-enabled fiber-bundle endomicroscopy platform and evaluated its preclinical feasibility for targeted fluorescence imaging in cervical cancer models. Methods: The platform integrates a portable fiber-bundle endomicroscopy (FBE) system, fluorescein isothiocyanate (FITC)-labeled candidate peptides, and a secure web-based telehealth platform for remote consultation. The FBE probe achieved a field of view of 1,700 µm and a lateral resolution of 4 µm, enabling cellular-level fluorescence imaging in a compact, portable format. Four FITC-labeled peptides (SHS1*, SHS2*, FPP*, and CRL*) were evaluated in A549, SiHa, and CaSki cell lines. Ex vivo testing was performed on commercial cervical tissue-array samples. The telehealth platform was assessed for secure medical-image/video transmission and end-to-end latency in a simulated remote-consultation setting. Results: Among the tested probes, FPP*-FITC and CRL*-FITC showed higher fluorescence-positive fractions in the p16-overexpressing cervical cancer cell lines than in the A549 comparator line, with the strongest signals observed in CaSki cells. In ex vivo testing, CRL*-FITC generated higher fluorescence intensity in malignant cervical tissue-array samples than in non-malignant comparator tissues, with a reported 4.6- to 7.4-fold difference in mean signal intensity (p < 0.001). The telehealth platform supported the secure transmission of medical images and video and demonstrated an end-to-end latency of <500 ms in a simulated remote consultation setting. Conclusions: These results support the technical and preclinical feasibility of integrating targeted fluorescence imaging, portable fiber-bundle endomicroscopy, and telehealth into a single platform. This study should therefore be interpreted as a preclinical feasibility study evaluating optical, molecular, and telehealth integration, rather than as a clinically validated cervical cancer screening test.

Piyawattanametha, Wibool

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