Jigsaw training-based background reverse attention transformer network for guidewire segmentation.

PURPOSE: Guidewire segmentation plays a crucial role in percutaneous coronary intervention. However, it is a challenging task due to the low signal-to-noise ratio of X-ray sequences and the great imbalance between the number of foreground and background pixels. Besides, most existing guidewire segmentation methods are designed for single guidewire segmentation. This paper aims to solve the task of single and dual guidewire segmentation in X-ray fluoroscopy sequences. METHODS: A jigsaw training-based background reverse attention (BRA) transformer network is proposed. A jigsaw training strategy is used to train the guidewire segmentation network. A BRA module is also designed to reduce the influence of background information. First, robust principal component is conducted to generate background maps for guidewire sequences. Then, BRA is computed on the basis of the background features. RESULTS: The experimental results on the dataset collected from three hospitals show that the proposed method can achieve single and dual guidewire segmentation in X-ray fluoroscopy sequences. Higher F1 score and precision than state-of-the-art guidewire segmentation methods can be obtained in most cases. CONCLUSION: The jigsaw training strategy helps reduce the need for dual guidewire data and improve the performance of the network. Our BRA module helps reduce the influence of background information and distinguish the guidewire. The proposed methods can obtain higher performance than state-of-the-art guidewire segmentation methods.

Direct evidence for kinetic effects associated with solar wind reconnection.

Kinetic effects resulting from the two-fluid physics play a crucial role in the fast collisionless reconnection, which is a process to explosively release massive energy stored in magnetic fields in space and astrophysical plasmas. In-situ observations in the Earth's magnetosphere provide solid consistence with theoretical models on the point that kinetic effects are required in the collisionless reconnection. However, all the observations associated with solar wind reconnection have been analyzed in the context of magnetohydrodynamics (MHD) although a lot of solar wind reconnection exhausts have been reported. Because of the absence of kinetic effects and substantial heating, whether the reconnections are still ongoing when they are detected in the solar wind remains unknown. Here, by dual-spacecraft observations, we report a solar wind reconnection with clear Hall magnetic fields. Its corresponding Alfvenic electron outflow jet, derived from the decouple between ions and electrons, is identified, showing direct evidence for kinetic effects that dominate the collisionless reconnection. The turbulence associated with the exhaust is a kind of background solar wind turbulence, implying that the reconnection generated turbulence has not much developed.

Modeling the reflectance of the lunar regolith by a new method combining Monte Carlo Ray tracing and Hapke's model with application to Chang'E-1 IIM data.

In this paper, we model the reflectance of the lunar regolith by a new method combining Monte Carlo ray tracing and Hapke's model. The existing modeling methods exploit either a radiative transfer model or a geometric optical model. However, the measured data from an Interference Imaging spectrometer (IIM) on an orbiter were affected not only by the composition of minerals but also by the environmental factors. These factors cannot be well addressed by a single model alone. Our method implemented Monte Carlo ray tracing for simulating the large-scale effects such as the reflection of topography of the lunar soil and Hapke's model for calculating the reflection intensity of the internal scattering effects of particles of the lunar soil. Therefore, both the large-scale and microscale effects are considered in our method, providing a more accurate modeling of the reflectance of the lunar regolith. Simulation results using the Lunar Soil Characterization Consortium (LSCC) data and Chang'E-1 elevation map show that our method is effective and useful. We have also applied our method to Chang'E-1 IIM data for removing the influence of lunar topography to the reflectance of the lunar soil and to generate more realistic visualizations of the lunar surface.