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Optical Imaging

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Machine and Deep Learning in Hyperspectral Fluorescence-Guided Brain Tumor Surgery.

Advances in experimental medicine and biology
Malignant glioma resection is often the first line of treatment in neuro-oncology. During glioma surgery, the discrimination of tumor's edges can be challenging at the infiltration zone, even by using surgical adjuncts such as fluorescence guidance (...

Imaging Evaluation of Peritoneal Metastasis: Current and Promising Techniques.

Korean journal of radiology
Early diagnosis, accurate assessment, and localization of peritoneal metastasis (PM) are essential for the selection of appropriate treatments and surgical guidance. However, available imaging modalities (computed tomography [CT], conventional magnet...

Deep Learning Empowered Fresnel-based Lensless Fluorescence Microscopy.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Miniaturized fluorescence microscopy has revolutionized the way neuroscientists study the brain in-vivo. Recent developments in computational lensless imaging promise a next generation of miniaturized microscopes in lensless fluorescence microscopy. ...

Image restoration for blurry optical images caused by photon diffusion with deep learning.

Journal of the Optical Society of America. A, Optics, image science, and vision
Optical macroscopic imaging techniques have shown great significance in the investigations of biomedical issues by revealing structural or functional information of living bodies through the detection of visible or near-infrared light derived from di...

Investigation of image plane for image reconstruction of objects through diffusers via deep learning.

Journal of biomedical optics
SIGNIFICANCE: The imaging of objects hidden in light-scattering media is a vital practical task in a wide range of applications, including biological imaging. Deep-learning-based methods have been used to reconstruct images behind scattering media un...

Spatial resolution improved fluorescence lifetime imaging via deep learning.

Optics express
We present a deep learning approach to obtain high-resolution (HR) fluorescence lifetime images from low-resolution (LR) images acquired from fluorescence lifetime imaging (FLIM) systems. We first proposed a theoretical method for training neural net...

Auto-focusing and quantitative phase imaging using deep learning for the incoherent illumination microscopy system.

Optics express
It is well known that the quantitative phase information which is vital in the biomedical study is hard to be directly obtained with bright-field microscopy under incoherent illumination. In addition, it is impossible to maintain the living sample in...

Deep Learning Applied to Automated Segmentation of Geographic Atrophy in Fundus Autofluorescence Images.

Translational vision science & technology
PURPOSE: This study describes the development of a deep learning algorithm based on the U-Net architecture for automated segmentation of geographic atrophy (GA) lesions in fundus autofluorescence (FAF) images.

High-Throughput Image Analysis of Lipid-Droplet-Bound Mitochondria.

Methods in molecular biology (Clifton, N.J.)
Changes to mitochondrial architecture are associated with various adaptive and pathogenic processes. However, quantification of changes to mitochondrial structures is limited by the yet unmet challenge of defining the borders of each individual mitoc...

Classification of marine microalgae using low-resolution Mueller matrix images and convolutional neural network.

Applied optics
In this paper, we used a convolutional neural network to study the classification of marine microalgae by using low-resolution Mueller matrix images. Mueller matrix images of 12 species of algae from 5 families were measured by a Mueller matrix micro...