Welcome to Gaire Laboratory!
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Research AREAS:
Research AREAS:
Our research focuses on developing novel computational methods and analysis tools for various application areas including biomedical imaging, biophotonics, machine vision, autonomy, RF, healthcare, and several other fields.
Some research areas we focused on:
AI, Deep Learning & Machine Learning, Computer Vision
Super-resolution Microscopy (Single-molecule localization microscopy (SMLM))
Biomedical Imaging, Image Analysis & Healthcare
Signal and Image Processing
Autonomy
Wireless Communication System
Optics and Photonics
csPWS-seg: Deep-learning-driven automatic nuclei segmentation for chromatin-sensitive partial wave spectroscopic (csPWS) microscopy
Click here to access the full paper in Optics Express journal!
Expansion and continuation of this project for cancer research is now supported by NSF EiR grant!
Chromatin-sensitive partial wave spectroscopic (csPWS) microscopy, a novel spectroscopic nanosensing technique, offers a noninvasive glimpse into the mass density distribution of cellular structures at the nanoscale, allowing the analysis of chromatin structure and organization and the global transcriptional state of the cell nuclei for the study of its role in carcinogenesis. Accurate segmentation of the nuclei in csPWS microscopy images is an essential step in isolating them for further analysis. Existing manual segmentation is error-prone, biased, time-consuming, and laborious, resulting in disrupted nuclear boundaries with partial or over-segmentation. We developed a deep-learning-based automatic nuclei segmentation algorithm called csPWS-seg for live cell images captured using csPWS microscopy. The automatic and accurate nuclei segmentation offered by the csPWS-seg enhances the reliability of chromatin analysis research, paving the way for more accurate diagnostics, treatment, and understanding of cellular mechanisms for cancer.
This is a collaborative work with the Backman laboratory at Northwestern University.
Related Publications:
[New] Md. Shahin Alom, Ali Daneshkhah, Nicolas Acosta, Nick Anthony, Emily Pujadas Liwag, Vadim Backman and Sunil Kumar Gaire, "Deep Learning-driven Automatic Nuclei Segmentation of Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging," Optics Express, 32 (25), pp. 45052-45074 (2024).
Md. Shahin Alom, Ali Daneshkhah, Nicolas Acosta, Nick Anthony, Emily Pujadas Liwag, Vadim Backman and Sunil Kumar Gaire, "Deep Learning-driven Automatic Nuclei Segmentation of Label-free Live Cell Chromatin-sensitive Partial Wave Spectroscopic Microscopy Imaging," bioRxiv (20 Aug 2024).
[Presented in SPIE Photonics West 2025 conference, Jan 2025] Md. Shahin Alom et al., "Automatic nuclei segmentation of label-free chromatin-sensitive partial wave spectroscopic microscopy using convolution neural network with transformer" Here is the link for the conference proceeding.
DsSMLM: Deep-learning Algorithm for Super-resolution Microscopy
This research is highlighted on the cover of Journal of Biomedical Optics Vol 26/No. 06. Link for the journal!
The 2014 Nobel Prize-winning technology, Super-resolution fluorescence microscopy, enables the imaging of cellular structures beyond the optical diffraction limit resolution (~250 nm), closer to the molecular scale. Among various super-resolution microscopy techniques, single-molecule localization microscopy (SMLM) such as Stochastic Optical Reconstruction Microscopy (STORM), Photo-activated Localization Microscopy (PALM), DNA Points Accumulation for imaging in nanoscale topography (DNA-PAINT), provides a spatial resolution of approximately 20 nm. Using spatial along with spectral information, a novel spectroscopic single-molecule localization microscopy (sSMLM) technique achieves even higher spatial resolution (sub-10 nm resolution). This technique also offers the unique advantage of simultaneous multicolor super-resolution imaging, visualizing multiple structures within the cell in a single round of acquisition.
Dr. Gaire is a trained researcher who uses STORM and sSMLM microscopy techniques for single-color and multicolor imaging. He worked on all aspects of SMLM imaging: instrumentation, sample preparation, image acquisition, algorithm development (classical and machine learning), and image analysis. In this project, his team developed a novel deep learning-based algorithm, DsSMLM, for the post-processing of sSMLM imaging data.
DsSMLM, a post processing algorithm for sSMLM imaging. (Image from publication [1]
DsSMLM performance on simulated sSMLM Data (a) Spatial image with ground truth coordinated (green plus) and DsSMLM predicted coordinates (orange circle); (b) Representative DsSMLM reconstructed spectral PSF compared with simulated and ground truth spectral PSF of AF647 dye. Scale bars=0.5 μm. (c) Spectral plots of images in (b). Noisy is the spectrum of simulated PSF. The emission spectrum from DsSMLM is smooth and very close to the ground-truth image with the matching peak. (Image from publication [1])
Related Publication:
Sunil Kumar Gaire, Ali Daneshkhah, Ethan Flowerday, Ruyi Gong, Jane Frederick, and Vadim Backman "Deep learning-based spectroscopic single-molecule localization microscopy," Journal of Biomedical Optics 29(6), 066501 (May 2024).
Sunil Kumar Gaire, et al. "Simultaneous Multicolor Spectroscopic Single-molecule Localization Microscopy Image Reconstruction using Machine Learning," Optica Imaging Congress, August 2023.
Sunil Kumar Gaire, Ethan Flowerday, Jane Frederick, Ruyi Gong, Sravya Prabhala, Leslie Ying, Hao F. Zhang, and Vadim Backman, "Deep Learning-based Spectroscopic Single-molecule Localization Microscopy for Simultaneous Multicolor Imaging, Imaging and Applied Optics Congress, 2022.
Computational approaches to accelerate SMLM techniques
Computational approaches to accelerate SMLM techniques
The SMLM technique provides higher spatial resolution but is inherently slow due to the requirements of imaging an extremely large number of frames (>10,000 frames) of biological samples to generate a high-quality super-resolution image. Therefore, accelerating image acquisition in SMLM has been of perennial interest. Dr. Gaire previously developed a couple of fast SMLM imaging techniques (using a reduced number of acquired frames) leveraging computational approaches. We used computational approaches such as deep learning and blind sparse inpainting to accelerate SMLM techniques. Specifically, during his Ph.D. Dr. Gaire worked on developing approaches to accelerate the following imaging techniques:
Three-dimensional (3D) SMLM
Multicolor spectroscopic SMLM (sSMLM).
For more details, see the links below to access the related publications (open-access).
Blind sparse inpainting reconstruction of a 3D Tubulin SMLM image. (a) Low-density; (b) reconstructed; and (c) high-density super-resolution 3D image with color indicating the depth of z. (Image from publication [1])
Deep learning reconstruction of dual-color simultaneously imaged sSMLM image of microtubules and mitochondria. (a) Low-density; (b) reconstructed; and (c) high-density super-resolution two-color image. (Image from publication [2])
Related publications:
Sunil Kumar Gaire, Yanhua Wang, Hao F. Zhang, Dong Liang, and Leslie Ying, "Accelerating 3D single-molecule localization microscopy using blind sparse inpainting," Journal of Biomedical Optics, 26(2), 026501, (2021).
Sunil Kumar Gaire, Yang Zhang, Hongyu Li, Ray Yu, Hao F. Zhang, and Leslie Ying, "Accelerating multicolor spectroscopic single-molecule localization microscopy using deep learning," Biomed. Opt. Express 11 (5), 2705-2721 (2020).
Sunil Kumar Gaire, Choyi Zhang, Hongyu Li, Peizhou Huang, Ruiying Liu, Haifeng Wang, Dong Liang, and Leslie Ying, "Accelerated 3D Localization Microscopy Using Blind Sparse Inpainting", IEEE ISBI 2019.
Dr Gaire Ph.D. dissertation: "Accelerating single-molecule localization microscopy using computational approaches"
Multilevel Orthogonal Coded Modulation:
Multilevel Orthogonal Coded Modulation:
Orthogonal codes are widely used in CDMA communication. These codes can also be used as error control codes in noisy communication systems. For channel coding using orthogonal codes, these codes are used to encode the information signal. The encoded data are modulated using spectrally efficient modulation techniques. In order to improve the error correction capability and improve the spectral efficiency, the orthogonal codes can be used as multilevel encoding combined with multilevel modulation techniques. The resulting system is spectrally efficient as well as able to correct a large number of errors. The encoder of the MOCM system is shown in the figure.
Master's thesis topic: "Multilevel orthogonal coded modulations".
Sunil Kumar Gaire, Saleh Faruque, "Performance Analysis of Orthogonal Coded M-Ary QAM Modulation Technique" CISS 2017, Baltimore, Maryland.
OptoRadio: a method of wireless communication using orthogonal M-ary PSK (OMPSK) modulations:
OptoRadio: a method of wireless communication using orthogonal M-ary PSK (OMPSK) modulations:
OptoRadio is a laser-based radio communication system using Orthogonal M-ary PSK Modulation. In this scheme, when a block of data needs to be transmitted, the corresponding block of the biorthogonal code is transmitted by means of multi-phase shift keying. At the receiver, two photodiodes are cross-coupled. The effect is that the net output power due to ambient light is close to zero. The laser signal is then transmitted only into one of the receivers. With all other signals being canceled out, the laser signal is an overwhelmingly dominant signal. The general block diagram of OptoRadio is shown in the figure.
Sunil Kumar Gaire, Saleh Faruque, Md. Maruf Ahamed, "OptoRadio: a method of wireless communication using orthogonal M-ary PSK (OMPSK) modulation", SPIE Optical Engineering+ Applications, 99790D-99790D-9.
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