Abstract
Fully automatic localization of anatomical structures in 2D and 3D radiological data sets is important in both computer aided diagnosis, and the rapid automatic processing of large amounts of data. We present a simple, accurate and fast approach with low computational complexity to find anatomical landmarks, based on a multi-scale regression codebook of informative image patches and encoded landmark contexts.
From a set of annotated training volumes the method captures the appearance of landmarks over several scales together with relative positions of neighboring landmarks and a spatial distribution model. During multi-scale search in a target volume, starting from the coarsest level, each landmark model predicts all landmark positions it has encoded, with the median of all predictions yielding the final prediction for each scale.
We present results on two challenging data sets (hand radiographs and hand CTs), where our method achieves comparable accuracy to the state of the art with substantially improved run-time.
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Donner, R., Menze, B.H., Bischof, H., Langs, G. (2013). Fast Anatomical Structure Localization Using Top-Down Image Patch Regression. In: Menze, B.H., Langs, G., Lu, L., Montillo, A., Tu, Z., Criminisi, A. (eds) Medical Computer Vision. Recognition Techniques and Applications in Medical Imaging. MCV 2012. Lecture Notes in Computer Science, vol 7766. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36620-8_14
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DOI: https://doi.org/10.1007/978-3-642-36620-8_14
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