This paper proposes a new algorithm for the e\u000efficient, automatic detection and localization of multiple anatomical structures within three-dimensional computed tomography (CT) scans. Applications include selective retrieval of patients images from PACS systems, semantic visual navigation and tracking radiation dose over time.<\/p>\n
The main contribution of this work is a new, continuous parametrization of the anatomy localization problem, which allows it to be addressed e\u000bffectively by multi-class random regression forests. Regression forests are similar to the more popular classi\ffication forests, but trained to predict continuous, multi-variate outputs, where the training focuses on maximizing the con\ffidence of output predictions. A single pass of our probabilistic algorithm
\nenables the direct mapping from voxels to organ location and size.<\/p>\n
Quantitative validation is performed on a database of 400 highly variable CT scans. We show that the proposed method is more accurate and robust than techniques based on e\u000ecient multi-atlas registration and template-based nearest-neighbour detection. Due to the simplicity of the regressor’s contextrich visual features and the algorithm’s parallelism, these results are achieved in typical run-times of only \u00184 seconds on a conventional single-core machine.<\/p>\n","protected":false},"excerpt":{"rendered":"
This paper proposes a new algorithm for the e\u000efficient, automatic detection and localization of multiple anatomical structures within three-dimensional computed tomography (CT) scans. Applications include selective retrieval of patients images from PACS systems, semantic visual navigation and tracking radiation dose over time. 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