Search results for "Volumetric imaging"
showing 3 items of 3 documents
Flexible multi-beam light-sheet fluorescence microscope for live imaging without striping artifacts
2018
The development of light-sheet fluorescence microscopy (LSFM) has greatly expanded the experimental capabilities in many biological and biomedical research fields, enabling for example live studies of murine and zebrafish neural activity or of cell growth and division. The key feature of the method is the selective illumination of a sample single plane, providing an intrinsic optical sectioning and allowing direct 2D image recording. On the other hand, this excitation scheme is more affected by absorption or scattering artifacts in comparison to point scanning methods, leading to un-even illumination. We present here an easily implementable method, based on acousto-optical deflectors (AOD),…
Comparative study of mandibular linear measurements obtained by cone beam computed tomography and digital calipers
2014
Objectives: Cone beam computed tomography (CBCT) is an innovative dental of imaging system characterized by rapid volumetric imaging with patient exposure to a single dose of radiation. The present study was carried out to compare the linear measurements obtained with CBCT and digital caliper in 20 mandibles from human cadavers. Study design: A total of 4800 linear measurements were measured between different mandibular anatomical points with CBCT and digital caliper. The real measurements were defined as those obtained with the digital caliper. Posteriorly, the mandibles were scanned to obtain the CBCT images, with software-based measurements of the distances. Results: The measurements obt…
Classification of diabetes-related retinal diseases using a deep learning approach in optical coherence tomography
2019
Background and objectives: Spectral Domain Optical Coherence Tomography (SD-OCT) is a volumetric imaging technique that allows measuring patterns between layers such as small amounts of fluid. Since 2012, automatic medical image analysis performance has steadily increased through the use of deep learning models that automatically learn relevant features for specific tasks, instead of designing visual features manually. Nevertheless, providing insights and interpretation of the predictions made by the model is still a challenge. This paper describes a deep learning model able to detect medically interpretable information in relevant images from a volume to classify diabetes-related retinal d…