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.g., scoliosis) and imaging artifacts (e.g. metal artifacts in MR). Our method aims to achieve this goal with two novel components. First, instead of treating vertebrae/discs as either repetitive components or completely independent entities, we emulate a radiologist and use a hierarchial strategy to learn detectors dedicated to anchor (distinctive) vertebrae, bundle (non-distinctive) vertebrae and inter-vertebral discs, respectively. At run-time, anchor vertebrae are detected concurrently to provide redundant and distributed appearance cues robust to local imaging artifacts. Bundle vertebrae detectors provide candidates of vertebrae with subtle appearance differences, whose labels are mutually determined by anchor vertebrae to gain additional robustness. Disc locations are derived from a cloud of responses from disc detectors, which is robust to sporadic voxel-level errors. Second, owing to the non-rigidness of spine anatomies, we employ a local articulated model to effectively model the spatial relations across vertebrae and discs. The local articulated model fuses appearance cues from different detectors in a way that is robust to abnormal spine geometry resulting from severe diseases. Our method is validated by 300 MR spine scout scans and exhibits robust performance, especially to cases with severe diseases and imaging artifacts.