Robust MR spine detection using hierarchical learning and local articulated model.

A clinically acceptable auto-spine detection system, i.e., localization and labeling of vertebrae and inter-vertebral discs, is required to have high robustness, in particular to severe diseases (e.

Robust automatic knee MR slice positioning through redundant and hierarchical anatomy detection.

Diagnostic magnetic resonance (MR) image quality is highly dependent on the position and orientation of the slice groups, due to the intrinsic high in-slice and low through-slice resolutions of MR imaging. Hence, the higher speed, accuracy, and reproducibility of automatic slice positioning, make it highly desirable over manual slice positioning. However, imaging artifacts, diseases, joint articulation, variations across ages and demographics as well as the extremely high performance requirements prevent state-of-the-art methods, such as volumetric registration, to be an off-the-shelf solution.

On-line automatic slice positioning for brain MR imaging.

In clinical brain MR imaging protocols, the technician collects a quick localizer and manually positions the subsequent scans using the localizer as a guide. We present a method for automatic slice positioning using a rapidly acquired 3D localizer. The localizer is automatically aligned to a statistical atlas representing 40 healthy subjects.