Attaining human-level performance for anatomical landmark detection in 3D CT data
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We present an efficient neural network approach for locating anatomical landmarks, using a two-pass, two-resolution cascaded approach which leverages a mechanism we term atlas location autocontext. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling the prediction of multiple volumetric heatmaps in a unified system, without prohibitive GPU memory requirements. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans and the neural network model is benchmarked against a previously reported decision forest model trained with the same cascaded system. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 observers, we show that the neural network reaches an accuracy which matches the annotator variability.
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