AIMC Topic: Tomography, Optical

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Convolutional neural networks for reconstruction of undersampled optical projection tomography data applied to in vivo imaging of zebrafish.

Journal of biophotonics
Optical projection tomography (OPT) is a 3D mesoscopic imaging modality that can utilize absorption or fluorescence contrast. 3D images can be rapidly reconstructed from tomographic data sets sampled with sufficient numbers of projection angles using...

Deep Learning Diffuse Optical Tomography.

IEEE transactions on medical imaging
Diffuse optical tomography (DOT) has been investigated as an alternative imaging modality for breast cancer detection thanks to its excellent contrast to hemoglobin oxidization level. However, due to the complicated non-linear photon scattering physi...

Exudate detection in fundus images using deeply-learnable features.

Computers in biology and medicine
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...

An Inversion Scheme for Hybrid Fluorescence Molecular Tomography Using a Fuzzy Inference System.

IEEE transactions on medical imaging
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...

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
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,...

The cytoarchitectonic landscape revealed by deep learning method facilitated precise positioning in mouse neocortex.

Cerebral cortex (New York, N.Y. : 1991)
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...

Image Reconstruction Using Deep Learning for Near-Infrared Optical Tomography: Generalization Assessment.

Advances in experimental medicine and biology
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...

Difference imaging from single measurements in diffuse optical tomography: a deep learning approach.

Journal of biomedical optics
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...

Monte Carlo-based data generation for efficient deep learning reconstruction of macroscopic diffuse optical tomography and topography applications.

Journal of biomedical optics
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...

Deep learning in macroscopic diffuse optical imaging.

Journal of biomedical optics
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...