A Steering Engine: Learning 3-D Anatomy Orientation Using Regression Forests

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 clinical 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 efficient way. Our work is distinguished by three main contributions. (1) Regression-based colatitude angle predictor: we use regression forests to model the highly non-linear mapping between appearance features and anatomy colatitude. (2) Iterative colatitude prediction scheme: we propose an algorithm that iteratively queries colatitude until longitude ambiguity is eliminated. (3) Rotation-invariant integral image: we design a spherical coordinates-based integral image from which Haar-like features of any orientation can be calculated efficiently. We validate our method on three diverse applications (different imaging 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 total of 400 scans), our method achieves a success rate above 90%. Experimental results suggest our method is fast, robust and accurate.