AI Medical Compendium Journal:
Journal of biomedical optics

Showing 51 to 60 of 103 articles

Comparison of preprocessing techniques to reduce nontissue-related variations in hyperspectral reflectance imaging.

Journal of biomedical optics
SIGNIFICANCE: Hyperspectral reflectance imaging can be used in medicine to identify tissue types, such as tumor tissue. Tissue classification algorithms are developed based on, e.g., machine learning or principle component analysis. For the developme...

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...

Detection of live breast cancer cells in bright-field microscopy images containing white blood cells by image analysis and deep learning.

Journal of biomedical optics
SIGNIFICANCE: Circulating tumor cells (CTCs) are important biomarkers for cancer management. Isolated CTCs from blood are stained to detect and enumerate CTCs. However, the staining process is laborious and moreover makes CTCs unsuitable for drug tes...

Deep learning on reflectance confocal microscopy improves Raman spectral diagnosis of basal cell carcinoma.

Journal of biomedical optics
SIGNIFICANCE: Raman spectroscopy (RS) provides an automated approach for assisting Mohs micrographic surgery for skin cancer diagnosis; however, the specificity of RS is limited by the high spectral similarity between tumors and normal tissues struct...

Framework for denoising Monte Carlo photon transport simulations using deep learning.

Journal of biomedical optics
SIGNIFICANCE: The Monte Carlo (MC) method is widely used as the gold-standard for modeling light propagation inside turbid media, such as human tissues, but combating its inherent stochastic noise requires one to simulate a large number photons, resu...

Optical bone densitometry robust to variation of soft tissue using machine learning techniques: validation by Monte Carlo simulation.

Journal of biomedical optics
SIGNIFICANCE: To achieve early detection of osteoporosis, a simple bone densitometry method using optics was proposed. However, individual differences in soft tissue structure and optical properties can cause errors in quantitative bone densitometry....

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...

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...

In vivo microscopy as an adjunctive tool to guide detection, diagnosis, and treatment.

Journal of biomedical optics
SIGNIFICANCE: There have been numerous academic and commercial efforts to develop high-resolution in vivo microscopes for a variety of clinical use cases, including early disease detection and surgical guidance. While many high-profile studies, comme...

Multispectral snapshot imaging of skin microcirculatory hemoglobin oxygen saturation using artificial neural networks trained on in vivo data.

Journal of biomedical optics
SIGNIFICANCE: Developing algorithms for estimating blood oxygenation from snapshot multispectral imaging (MSI) data is challenging due to the complexity of sensor characteristics and photon transport modeling in tissue. We circumvent this using a met...