Most virtual reality (VR) applications use a commercial controller for interaction. However, a typical virtual reality controller (VRC) lacks positional precision and accu-racy in millimeter-scale scenarios. This lack of precision and accuracy is caused by built-in sensors drift. Therefore, the tracking performance of a VRC needs to be enhanced for millimeter-scale scenarios. Herein, we introduce a novel way of enhancing the tracking performance of a commercial VRC in a millimeter-scale environment using a deep learning (DL) al-gorithm. Specifically, we use a long short-term memory (LSTM) model trained with data collected from a linear motor, an IMU sensor, and a VRC. We integrate the virtual environment developed in Unity software with the LSTM model running in Python. We designed three experimental conditions: the VRC, Kalman filter (KF), and LSTM modes. Furthermore, we evaluate tracking performances in the three conditions and two other experimental scenarios, namely stationary and dynamic. In the stationary experimental scenario, the system is left motionless for 10 s. By contrast, in the dynamic experimental scenarios, the linear stage moves the system by 12 mm along the X, Y, and Z axes. The experimental results indicate that the deep learning model outperforms the standard controllers positional performance by 85.69 % and 92.14 % in static and dynamic situations, respectively.

Quantification of cerebral white matter hyperintensities (WMH) of presumed vascular origin is of key importance in many neurological research studies. Currently, measurements are often still obtained from manual segmentations on brain MR images, which is a laborious procedure. The automatic WMH segmentation methods exist, but a standardized comparison of the performance of such methods is lacking. We organized a scientific challenge, in which developers could evaluate their methods on a standardized multi-center/-scanner image dataset, giving an objective comparison: the WMH Segmentation Challenge. Sixty T1 + FLAIR images from three MR scanners were released with the manual WMH segmentations for training. A test set of 110 images from five MR scanners was used for evaluation. The segmentation methods had to be containerized and submitted to the challenge organizers. Five evaluation metrics were used to rank the methods: 1) Dice similarity coefficient; 2) modified Hausdorff distance (95th percentile); 3) absolute log-transformed volume difference; 4) sensitivity for detecting individual lesions; and 5) F1-score for individual lesions. In addition, the methods were ranked on their inter-scanner robustness; 20 participants submitted their methods for evaluation. This paper provides a detailed analysis of the results. In brief, there is a cluster of four methods that rank significantly better than the other methods, with one clear winner. The inter-scanner robustness ranking shows that not all the methods generalize to unseen scanners. The challenge remains open for future submissions and provides a public platform for method evaluation.