Biophotonics Modalities for High-Resolution Imaging of Microcirculatory Tissue Beds Using Endogenous Contrast: A Review on Present Scenario and Prospects

The microcirculation is a complex system, and the visualization of microcirculation has great significance in improving our understanding of pathophysiological processes in various disease conditions, in both clinical and fundamental studies. A range of techniques are available or emerging for investigating different aspect of the microcirculation in animals and humans. This paper reviews the recent developments in the field of high-resolution and high-sensitive optical imaging of microcirculatory tissue beds, emphasizing technologies that utilize the endogenous contrast mechanism. Optical imaging techniques such as intravital microscopy, Capillaroscopy, laser Doppler perfusion imaging, laser speckle perfusion imaging, polarization spectroscopy, photo-acoustic tomography, and various implementations of optical coherence tomography based on Doppler and speckle contrast imaging are presented together with their prospectives and challenges

Automatic Tooth Segmentation from 3D Dental Model using Deep Learning: A Quantitative Analysis of what can be learnt from a Single 3D Dental Model

3D tooth segmentation is an important task for digital orthodontics. Several Deep Learning methods have been proposed for automatic tooth segmentation from 3D dental models or intraoral scans. These methods require annotated 3D intraoral scans. Manually annotating 3D intraoral scans is a laborious task. One approach is to devise self-supervision methods to reduce the manual labeling effort. Compared to other types of point cloud data like scene point cloud or shape point cloud data, 3D tooth point cloud data has a very regular structure and a strong shape prior. We look at how much representative information can be learnt from a single 3D intraoral scan. We evaluate this quantitatively with the help of ten different methods of which six are generic point cloud segmentation methods whereas the other four are tooth segmentation specific methods. Surprisingly, we find that with a single 3D intraoral scan training, the Dice score can be as high as 0.86 whereas the full training set gives Dice score of 0.94. We conclude that the segmentation methods can learn a great deal of information from a single 3D tooth point cloud scan under suitable conditions e.g. data augmentation. We are the first to quantitatively evaluate and demonstrate the representation learning capability of Deep Learning methods from a single 3D intraoral scan. This can enable building self-supervision methods for tooth segmentation under extreme data limitation scenario by leveraging the available data to the fullest possible extent.Comment: accepted to SIPAIM 202

Review Article Biophotonics Modalities for High-Resolution Imaging of Microcirculatory Tissue Beds Using Endogenous Contrast: A Review on Present Scenario and Prospects

The microcirculation is a complex system, and the visualization of microcirculation has great significance in improving our understanding of pathophysiological processes in various disease conditions, in both clinical and fundamental studies. A range of techniques are available or emerging for investigating different aspect of the microcirculation in animals and humans. This paper reviews the recent developments in the field of high-resolution and high-sensitive optical imaging of microcirculatory tissue beds, emphasizing technologies that utilize the endogenous contrast mechanism. Optical imaging techniques such as intravital microscopy, Capillaroscopy, laser Doppler perfusion imaging, laser speckle perfusion imaging, polarization spectroscopy, photoacoustic tomography, and various implementations of optical coherence tomography based on Doppler and speckle contrast imaging are presented together with their prospectives and challenges

Split-spectrum amplitude-decorrelation angiography with optical coherence tomography

Amplitude decorrelation measurement is sensitive to transverse flow and immune to phase noise in comparison to Doppler and other phase-based approaches. However, the high axial resolution of OCT makes it very sensitive to the pulsatile bulk motion noise in the axial direction. To overcome this limitation, we developed split-spectrum amplitude-decorrelation angiography (SSADA) to improve the signal-to-noise ratio (SNR) of flow detection. The full OCT spectrum was split into several narrower bands. Inter-B-scan decorrelation was computed using the spectral bands separately and then averaged. The SSADA algorithm was tested on in vivo images of the human macula and optic nerve head. It significantly improved both SNR for flow detection and connectivity of microvascular network when compared to other amplitude-decorrelation algorithms.National Institutes of Health (U.S.) (Grant R01 EY013516)National Institutes of Health (U.S.) (Grant R01-EY11289-26)United States. Air Force Office of Scientific Research (FA9550-10-1-0551