AIMC Topic: Tomography, Optical

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Exudate detection in fundus images using deeply-learnable features.

Computers in biology and medicine Nov 3, 2018
Presence of exudates on a retina is an early sign of diabetic retinopathy, and automatic detection of these can improve the diagnosis of the disease. Convolutional Neural Networks (CNNs) have been used for automatic exudate detection, but with poor p...
Tomography, Optical Databases, Factual Humans Deep Learning Image Processing, Computer-Assisted Diabetic Retinopathy Fundus Oculi
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An Inversion Scheme for Hybrid Fluorescence Molecular Tomography Using a Fuzzy Inference System.

IEEE transactions on medical imaging Sep 1, 2015
The imaging performance of fluorescence molecular tomography (FMT) improves when information from the underlying anatomy is incorporated into the inversion scheme, in the form of priors. The requirement for incorporation of priors has recently driven...
Mice, Nude Molecular Imaging Phantoms, Imaging Mice Tomography, Optical Animals Fuzzy Logic Image Processing, Computer-Assisted
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FMT-ReconNet: Fluorescence Molecular Tomography Reconstruction using Prior Knowledge and Deformation Neural Network.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference Jul 1, 2024
Fluorescence molecular tomography (FMT) is a powerful imaging technique for 3D reconstruction of internal fluorescent sources. However, its spatial resolution is limited by a simplified forward model and an ill-posed inverse problem. To address this,...
Neural Networks, Computer Algorithms Humans Fluorescence Tomography, Optical Image Processing, Computer-Assisted Imaging, Three-Dimensional Tomography
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The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex.

Cerebral cortex (New York, N.Y. : 1991) Jun 4, 2024
Neocortex is a complex structure with different cortical sublayers and regions. However, the precise positioning of cortical regions can be challenging due to the absence of distinct landmarks without special preparation. To address this challenge, w...
Tomography, Optical Imaging, Three-Dimensional Mice Male Animals Neurons Deep Learning Neocortex Mice, Inbred C57BL
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Image Reconstruction Using Deep Learning for Near-Infrared Optical Tomography: Generalization Assessment.

Advances in experimental medicine and biology Jan 1, 2023
Time is one of the most critical factors in preventing brain lesions due to hypoxic ischemia in preterm infants. Since early detection of low oxygenation is vital and the time window for therapy is narrow, near-infrared optical tomography (NIROT) mus...
Humans Neural Networks, Computer Image Processing, Computer-Assisted Infant, Premature Phantoms, Imaging Infant, Newborn Deep Learning Tomography, Optical
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Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.

Journal of biomedical optics Aug 1, 2022
SIGNIFICANCE: "Difference imaging," which reconstructs target optical properties using measurements with and without target information, is often used in diffuse optical tomography (DOT) in vivo imaging. However, taking additional reference measureme...
Phantoms, Imaging Tomography, Optical Humans Algorithms Image Processing, Computer-Assisted Artifacts Deep Learning
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Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications.

Journal of biomedical optics Apr 1, 2022
SIGNIFICANCE: Deep learning (DL) models are being increasingly developed to map sensor data to the image domain directly. However, DL methodologies are data-driven and require large and diverse data sets to provide robust and accurate image formation...
Monte Carlo Method Tomography, Optical Deep Learning
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Deep learning in macroscopic diffuse optical imaging.

Journal of biomedical optics Feb 1, 2022
SIGNIFICANCE: Biomedical optics system design, image formation, and image analysis have primarily been guided by classical physical modeling and signal processing methodologies. Recently, however, deep learning (DL) has become a major paradigm in com...
Tomography, Optical Humans Computer Simulation Image Processing, Computer-Assisted Deep Learning Spectrum Analysis
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Deep learning-based method to accurately estimate breast tissue optical properties in the presence of the chest wall.

Journal of biomedical optics Oct 1, 2021
SIGNIFICANCE: In general, image reconstruction methods used in diffuse optical tomography (DOT) are based on diffusion approximation, and they consider the breast tissue as a homogenous, semi-infinite medium. However, the semi-infinite medium assumpt...
Humans Thoracic Wall Neural Networks, Computer Tomography, Optical Deep Learning
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Convolutional neural network-based approach to estimate bulk optical properties in diffuse optical tomography.

Applied optics Feb 10, 2020
Deep learning has been actively investigated for various applications such as image classification, computer vision, and regression tasks, and it has shown state-of-the-art performance. In diffuse optical tomography (DOT), the accurate estimation of ...
Breast Humans Light Computer Simulation Tomography, Optical Deep Learning Scattering, Radiation Image Processing, Computer-Assisted Models, Theoretical Time Factors
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