Timely intervention in growing individuals, such as brace treatment, relies on early detection of adolescent idiopathic scoliosis (AIS). To this end, several screening methods have been implemented. However, these methods have limitations in predicting the Cobb angle. This study aimed to evaluate the performance of a three-dimensional depth sensor imaging system with a deep learning algorithm (DLA), in predicting the Cobb angle in AIS. Retrospective analysis of prospectively collected, consecutive, nonrandomized series of patients at five scoliosis centers in Japan. One hundred and-sixty human subjects suspected to have AIS were included. Patient demographics, radiographic measurements, and predicted Cobb angle derived from the DLA were the outcome measures for this study. One hundred and sixty data files were shuffled into five datasets with 32 data files at random (dataset 1, 2, 3, 4, and 5) and five-fold cross validation was performed. The relationships between the actual and predicted Cobb angles were calculated using Pearson's correlation coefficient analyses. The prediction performances of the network models were evaluated using mean absolute error (MAE) and root mean square error (RMSE) between the actual and predicted Cobb angles. The shuffling into five datasets and five-fold cross validation was conducted ten times. There were no study-specific biases related to conflicts of interest. The correlation between the actual and the mean predicted Cobb angles was 0.91. The MAE and RMSE were 4.0° and 5.4°, respectively. The accuracy of the mean predicted Cobb angle was 94% for identifying a Cobb angle of ≥10° and 89% for that of ≥20°. The three-dimensional depth sensor imaging system with its newly innovated convolutional neural network for regression is objective and has significant ability to predict the Cobb angle in children and adolescents. This system is expected to be used for screening scoliosis in clinics or physical examination at schools.

Recently, many countries have faced serious problems associated with aging civil infrastructures such as bridges, tunnels, dams, highways and so on. Aging infrastructures are increasing year by year and suitable maintenance actions are necessary to maintain their safety and serviceability. In fact, infrastructure deterioration has caused serious problems in the past. In order to prevent accidents with civil infrastructures, supervisors must spend a lot of money to maintain the safe conditions of infrastructures. Therefore, new technologies are required to reduce maintenance costs. In 2014 the Japanese government started the Cross-Ministerial Strategic Innovation Promotion Program (SIP), and technologies for infrastructure maintenance have been studied in the SIP project [1]. Fujitsu Limited, Hokkaido University, The University of Tokyo, Nagoya Institute of Technology and Docon Co. Limited have been engaged in the SIP project to develop a bridge inspection support system using information technology and robotic technology. Our system is divided into the following two main parts: bridge inspection support robots using a two-wheeled multicopter, and an inspection data management system utilizing 3D modeling technology. In this paper, we report the bridge inspection support system developed in our SIP project.

In this study, a novel algorithm for cylinder-based registration and model fitting of laser-scanned point clouds was proposed. The algorithm was designed specifically for as-built modeling of a plant piping system. In contrast with Iterative Closest Point (ICP)-based methods, fine registration and model fitting were performed simultaneously, by solving a single nonlinear constraint equation. This prevented alignment error arising in registration from propagating to model fitting. Coarse registration was automated by identifying cylindrical surfaces and by finding matches among their cylinder axes using a random sample consensus (RANSAC) method. The accuracy and robustness of the proposed algorithm was first confirmed using scan simulations. It performed well even when there was zero overlap between scans, and was demonstrated to achieve better modeling accuracy than ICP-based methods. The algorithm was then applied to point clouds scanned from a real plant, with successful results. The proposed algorithm achieves as-built modeling accuracies that would be fully acceptable when conducting renovation work on existing piping systems.

Recently, by Mobile Mapping System (MMS) with laser scanners, a GPS and IMU (Inertial Measurement Unit), 3D point clouds of urban areas (MMS point clouds) are easily acquired. When the same areas are scanned several times by the MMS, the point clouds often have differences in the range of several hundreds of millimetres. Such differences are caused by inertial drifts of IMU and losses of GPS signals in urban areas. In this paper, we propose an automatic accurate registration method of MMS point clouds using a new variant of ICP (Iterative Closest Point) algorithm for MMS point clouds and trajectory modification. Our method consists of four steps. Firstly, some trajectory points are automatically extracted by analyzing the trajectory. Secondly, the differences of point clouds are derived at the extracted trajectory points in the overlapping scan region by our new ICP algorithm which minimizes pointto- plane and point-to-point distances simultaneously and filters incorrect correspondences based on a point classification by PCA (Principle Component Analysis). Thirdly, the modified positions and rotation parameters at all extracted trajectory points are derived by a least squares method for positioning and registration constraints. Finally, each point in the point clouds is modified by coordinate transformations which are derived from linear interpolation of the modified positions and rotation parameters of the extracted trajectory points. Our method was applied to MMS point clouds and trajectory and the performances were evaluated.