Detection and location of 127 anatomical landmarks in diverse CT datasets


The automatic detection and localization of anatomical landmarks has wide application, including intra and interpatient registration, study location and navigation, and the targeting of specialized algorithms. In this paper, we demonstrate the automatic detection and localization of 127 anatomically defined landmarks distributed throughout the body, excluding arms. Landmarks are defined on the skeleton, vasculature and major organs. Our approach builds on the classification forests method, using this classifier with simple image features which can be efficiently computed. For the training and validation of the method we have used 369 CT volumes on which radiographers and anatomists have marked ground truth (GT) — that is the locations of all defined landmarks occurring in that volume. A particular challenge is to deal with the wide diversity of datasets encountered in radiology practice. These include data from all major scanner manufacturers, different extents covering single and multiple body compartments, truncated cardiac acquisitions, with and without contrast. Cases with stents and catheters are also represented. Validation is by a leave-one-out method, which we show can be efficiently implemented in the context of decision forest methods. Mean location accuracy of detected landmarks is 13.45mm overall; execution time averages 7s per volume on a modern server machine. We also present localization ROC analysis to characterize detection accuracy — that is to decide if a landmark is or is not present in a given dataset.

DOI: 10.1117/12.2039157

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@inproceedings{Dabbah2014DetectionAL, title={Detection and location of 127 anatomical landmarks in diverse CT datasets}, author={Mohammad A. Dabbah and Sean Murphy and Hippolyte Pello and Romain Courbon and Erin Beveridge and Stewart Wiseman and Daniel Wyeth and Ian Poole}, booktitle={Medical Imaging: Image Processing}, year={2014} }