Automated three-dimensional vessel reconstruction based on deep segmentation and bi-plane angiographic projections.

Automated three-dimensional (3D) blood vessel reconstruction to improve vascular diagnosis and therapeutics is a challenging task in which the real-time implementation of automatic segmentation and specific vessel tracking for matching artery sequences is essential. Recently, a deep learning-based segmentation technique has been proposed; however, existing state-of-the-art deep architectures exhibit reduced performance when they are employed using real in-vivo imaging because of serious issues such as low contrast and noise contamination of the X-ray images. To overcome these limitations, we propose a novel methodology composed of the de-haze image enhancement technique as pre-processing and multi-level thresholding as post-processing to be applied to the lightweight multi-resolution U-shaped architecture.

Real-time microrobot posture recognition via biplane X-ray imaging system for external electromagnetic actuation.

Purpose: As a promising intravascular therapeutic approach for autonomous catheterization, especially for thrombosis treatment, a microrobot or robotic catheter driven by an external electromagnetic actuation system has been recently investigated. However, the three-dimensional (3D) real-time position and orientation tracking of the microrobot remains a challenge for precise feedback control in clinical applications owing to the micro-size of the microrobot geometry in vessels, along with bifurcation and vulnerability. Therefore, in this paper, we propose a 3D posture recognition method for the unmanned microrobotic surgery driven by an external electromagnetic actuator system.