Digital toolbox for vector field characterization

Vectorial structured light fields have displayed properties advantageous in many disciplines ranging from communications, microscopy and metrology to laser cutting and characterizing quantum channels. The generation of these fields has been made convenient through the implementation of nanophotonic metasurfaces amongst other static and digital techniques. Consequently, the detection and characterisation of these fields is of equal importance. Most existing techniques involve using separate polarization optics and correlation filters to perform the projective measurements – or are only able to perform such measurements on a subset of possible vector states. We present a compact, fully automated measurement technique based on a digital micro-mirror device (DMD), which facilitates the complete, local and global, characterisation of the spatial mode and polarization degrees-of-freedom (DOFs) for arbitrary vectorial fields. We demonstrate our approach through the identification of relevant hybrid-order Poincaré spheres, the reconstruction of state vectors on these spheres, as well as the recovery of the non-separability and states-of-polarization for a variety of vector beams.

Improvement of Automatic Measurement Evaluation System for Subway Structures by Adjacent Excavation

This study evaluated the structural stability of subway structures based on adjacent excavations by comparing automatically measured and numerically analyzed data. The reliability of the automated measurement methodology was evaluated by first applying probability statistical analysis to the measured results and then comparing these results with the numerically analyzed results. An improvement in the calculation method evaluation system, including the method of processing and analysis of the automatically measured data of subway structures through the average value of probability density, was proposed. As a result of the field measurement and numerical analysis, the measured results of tunnel displacement and track deformation exhibited some differences. However, it was determined that the construction stage and location where the maximum values of the tunnel displacement and track deformation occurred had similarities.

314 Automated left atrial volume measurement by two-dimensional speckle-tracking echocardiograpy. Feasibility, accuracy, and reproducibility

Aims A by-product of left atrial (LA) strain analysis is the automated measurement of LA maximal volume (LAVmax), which may decrease the time of echocardiography reporting, and increase the reproducibility of the LAVmax measurement. However, the automated measurement of LAVmax by two-dimensional speckle-tracking analysis (2DSTE) has never been validated. Accordingly, we sought to: (i) assess the feasibility of automated LAVmax measurement by 2DSTE; (ii) compare the automated LAVmax by 2DSTE with conventional two-dimensional (2DE) biplane and three-dimensional echocardiography (3DE) measurements; and (iii) evaluate the accuracy and reproducibility of the three echocardiography techniques. Methods and results LAVmax (34–197 ml) were obtained from 198/210 (feasibility 94%) consecutive patients with various cardiac diseases (median age 67 years, 126 men) by 2DSTE, 2DE, and 3DE. 2DE and 2DSTE measurements resulted in similar LAVmax values (bias = 1.5 ml, limits of agreement, LOA ± 7.5 ml), and slightly underestimated 3DE LAVmax (biases = −5 ml, LOA ± 17 ml, and −6 ml, LOA ± 16 ml, respectively). LAVmax by 2DSTE and 2DE were strongly correlated to those obtained by cardiac magnetic resonance (CMR) (r = 0.946, and r = 0.935, respectively; P < 0.001). However, LAVmax obtained by 2DSTE (bias = −9.5 ml, LOA ± 16 ml) and 2DE (bias = −8 ml, LOA ± 17 ml) were significantly smaller than those measured by CMR. Conversely, 3DE LAVmax were similar to CMR (bias = −2 ml, LOA ± 10 ml). Excellent intra- and inter-observer intraclass correlations were found for 3DE (0.995 and 0.995), 2DE (0.990 and 0.988), and 2DSTE (0.990 and 0.989). Conclusions Automated LAVmax measurement by 2DSTE is highly feasible, highly reproducible, and provided similar values to conventional 2DE calculations in consecutive patients with a wide range of LAVmax.

Comparison of manual versus automated measurement of Cobb angle in idiopathic scoliosis based on a deep learning keypoint detection technology

Purpose The present study compared manual and automated measurement of Cobb angle in idiopathic scoliosis based on deep learning keypoint detection technology. Methods A total of 181 anterior–posterior spinal X-rays were included in this study, including 165 cases of idiopathic scoliosis and 16 normal adult cases without scoliosis. We labeled all images and randomly chose 145 as the training set and 36 as the test set. Two state-of-the-art deep learning object detection models based on convolutional neural networks were used in sequence to segment each vertebra and locate the vertebral corners. Cobb angles measured from the output of the models were compared to manual measurements performed by orthopedic experts. Results The mean Cobb angle in test cases was 27.4° ± 19.2° (range 0.00–91.00°) with manual measurements and 26.4° ± 18.9° (range 0.00–88.00°) with automated measurements. The automated method needed 4.45 s on average to measure each radiograph. The intra-class correlation coefficient (ICC) for the reliability of the automated measurement of the Cobb angle was 0.994. The Pearson correlation coefficient and mean absolute error between automated positioning and expert annotation were 0.990 and 2.2° ± 2.0°, respectively. The analytical result for the Spearman rank-order correlation was 0.984 (p < 0.001). Conclusion The automated measurement results agreed with the experts’ annotation and had a high degree of reliability when the Cobb angle did not exceed 90° and could locate multiple curves in the same scoliosis case simultaneously in a short period of time. Our results need to be verified in more cases in the future.

Measurement of Intima-Media Thickness Depending on Intima Media Complex Segmentation by Deep Neural Networks

Intima Media Thickness (IMT) of the carotid artery is an important marker indicating the sign of cardiovascular disease. Automated measurement of IMT requires segmentation of intima media complex (IMC).Traditional methods which use shape, color and texture for classification have poor generalization capability. This paper proposes two models- the pipeline model and the end-to-end model using Convolutional Neural Networks (CNN) and auto encoder–decoder network respectively. CNN architecture is implemented and tested by varying the number of convolutional layer, size of the kernel as well as the number of kernels. Auto encoder–decoder performs pixel wise classification using two interconnected pathways for identifying the boundary of lumen-intima (LI) and media adventitia (MA). This helps in reconstruction of the segmented portion for measurement of IMT. Both methods are tested using a dataset of 550 subjects. The results clearly indicate that end-to-end model has an edge over the pipeline model exhibiting lesser deviation between the automated measurement and the measurement made by the radiologist. The pipeline model however has better segmentation accuracy when the size of the image used for training is small. The convolutional neural network with auto encoder–decoder proves robust through sparse representation, and faster learning with better generalization. Also, the experimental setup is analyzed by interconnecting Tensor flow simulated result with Raspberry PI and the outcomes are analyzed.

Automation of procedure for diagnostics of thin film electroluminescent indicators’ structure

Problems of measurement automation for creating a testing system are considered. This system accelerates the process of parameters controlling in the production and development of new samples of indicator (display) technology. The main parameters of the indicators are defined, the tasks of automation of measuring processes are indicated, the hardware and software structure of the system is developed. The main features of an integrated approach to solve the problems of automating the process of testing the structures of thin-film electroluminescent indicators were proposed, the parameters of thin-film electroluminescent indicators were considered as the basis for forming the composition of an automated testing complex, and the automation of processing the experimental results at the software level was described. The ideas outlined in this article make it possible to formulate a technical task for the development of a complex for automated measurement of the parameters of thin-film electroluminescent elements, as well as its components and software.