Automatic algorithm for the characterization of sweat ducts in a three-dimensional fingerprint.

In this study, an automatic algorithm has been presented based on a convolutional neural network (CNN) employing U-net. An ellipsoid and an ellipse were applied for approximation of a three-dimensional sweat duct and en face sweat pore at the different depths, respectively. The results demonstrated that the length and the diameter of the ellipsoid can be used to quantitatively describe the sweat ducts, which has a potential for estimating the frequency of resonance in millimeter (mm) wave and terahertz (THz) wave. In addition, projection-based sweat pores were extracted to overcome the effect that the diameters of en face sweat pores depend on the depth. Finally, the projection-based image of sweat pores was superposed with a maximum intensity projection (MIP)-based internal fingerprint to construct a hybrid internal fingerprint, which can be applied for identification recognition and information encryption.

Three-dimensional morphological characterization of blood droplets during the dynamic coagulation process.

In this study, we employed a method integrating optical coherence tomography (OCT) with the U-Net and Visual Geometry Group (VGG)-Net frameworks within a convolutional neural network for quantitative characterization of the three dimensional whole blood during the dynamic coagulation process. VGG-Net architecture for the identification of blood droplets across three distinct coagulation stages including drop, gelation, and coagulation achieves an accuracy of up to 99%. In addition, the U-Net architecture demonstrated proficiency in effectively segmenting uncoagulated and coagulated portions of whole blood, as well as the background. Notably, parameters such as volume of uncoagulated and coagulated segments of the whole blood were successfully employed for the precise quantification of the coagulation process, which indicates well for the potential of future clinical diagnostics and analyses.

Polarization coherency matrix tomography.

In this paper, a polarization-sensitive optical coherence tomography (PS-OCT) based polarization coherency matrix tomography (PCMT) combining polarization coherency matrix with Mueller matrix is proposed for the determination of complete polarization properties of tissue. PCMT measures the Jones matrix of biological sample based on similar transformation, in which four elements have initial random phase from different polarization states based on traditional PS-OCT. The results indicate that PCMT can eliminate the phase difference of incident lights with different polarization states. In addition, the polarization coherency matrix using three polarization states has complete information of the sample Jones matrix. Finally, the 16 elements of the sample Mueller matrix are applied for deriving fully polarized optical properties of the sample based on the elliptical diattenuator and the elliptical retarder. Thus, the method based on the PCM and Mueller matrix has the advantage over the traditional PS-OCT.

Orthogonal-polarization-gating optical coherence tomography for human sweat ducts in vivo.

We propose an orthogonal-polarization-gating optical coherence tomography (OPG-OCT) for human sweat ducts in vivo. OPG-OCT is composed of the orthogonal linearly polarized light of a sample arm individually interfering with orthogonal linearly polarized lights of the reference arms, where OPG-OCT induces two images, one reflecting the projection intensity and the other the horizontal linear diattenuation (HLD). The results demonstrate that OPG-OCT projection intensity could improve the image quality of sweat ducts. HLD also clearly illustrates the spiral shape of the sweat ducts. Finally, sweat ducts in intensity image are segmented by employing convolutional neural networks (CNN). The proportions of left-handed and right-handed ducts are extracted to characterize the sweat ducts based on HLD. Therefore, the OPG-OCT technique employing CNN for the human sweat glands has the potential to automatically identify the human sweat ducts in vivo.