Automatic Semantic Parsing of CT Scans via Multiple Randomized Decision Trees

We introduce a new, efficient algorithm for the automatic detection and localization of anatomical structures within 3D CT images.

Our algorithm builds upon recent randomized decision tree classifiers and produces accurate posterior probabilities for each of the classes (e.g. organ labels) in the training set. Accurate results are obtained by exploiting the high level of generalization offered by the classifier. Furthermore, its massive parallelism yields high computational efficiency.

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In  Radiological Society of North America (RSNA)

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