Anatomical structures have intrinsic orientations along one or more directions, e.g., the tangent directions at points along a vessel central-line, the normal direction of an inter-vertebral disc or the base-to-apex direction of the heart. Although auto-detection of anatomy orientation is critical to various clin- ical applications, it is much less explored compared to its peer, "auto-detection of anatomy location". In this paper, we propose a novel and generic algorithm, named as "steering engine", to detect anatomy orientation in a robust and effi- cient way. Our work is distinguished by three main contributions. (1) Regres- sion-based colatitude angle predictor: we use regression forests to model the highly non-linear mapping between appearance features and anatomy colati- tude. (2) Iterative colatitude prediction scheme: we propose an algorithm that iteratively queries colatitude until longitude ambiguity is eliminated. (3) Rota- tion-invariant integral image: we design a spherical coordinates-based integral image from which Haar-like features of any orientation can be calculated effi- ciently. We validate our method on three diverse applications (different imag- ing modalities and organ systems), i.e., vertebral column tracing in CT, aorta tracing in CT and spinal cord tracing in MRI. In all applications (tested on a to- tal of 400 scans), our method achieves a success rate above 90%. Experimental results suggest our method is fast, robust and accurate.
['Fitsum A. Reda', 'Yiqiang Zhan', 'Xiang Sean Zhou']
A Steering Engine: Learning 3-D Anatomy Orientation Using Regression Forests