Regression Forests for Efficient Anatomy Detection and Localization in CT Studies

This paper proposes multi-class random regression forests as an algorithm for the efficient, automatic detection and localization of anatomical structures within three-dimensional CT scans. Regression forests are similar to the more popular classification forests, but trained to predict continuous outputs. We introduce a new, continuous parametrization of the anatomy localization task which is effectively addressed by regression forests. This is shown to be a more natural approach than classification. A single pass of our probabilistic algorithm enables the direct mapping from voxels to organ location and size; with training focusing on maximizing the confidence of output predictions. As a by-product, our method produces salient anatomical landmarks; i.e. automatically selected "anchor" regions which help localize organs of interest with high confidence. Quantitative validation is performed on a database of 100 highly variable CT scans. Localization errors are shown to be lower (and more stable) than those from global affine registration approaches. The regressor's parallelism and the simplicity of its context-rich visual features yield typical runtimes of only 1s. Applications include semantic visual navigation, image tagging for retrieval, and initializing organ-specific processing.