Automatic Extraction of Anatomical Landmarks From Medical Image Data: An Evaluation of Different Methods

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Automatic Extraction of Anatomical Landmarks From Medical Image Data: An Evaluation of Different Methods Kainmueller Lab

Kainmueller D, Hege Hans-Christian, Seim Heiko, Heller Markus, Zachow Stefan

Note: Research in this publication was not performed at Janelia.

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Abstract

This work presents three different methods for automatic detection of anatomical landmarks in CT data, namely for the left and right anterior superior iliac spines and the pubic symphysis. The methods exhibit different degrees of generality in terms of portability to other anatomical landmarks and require a different amount of training data. The ſrst method is problem-speciſc and is based on the convex hull of the pelvis. Method two is a more generic approach based on a statistical shape model including the landmarks of interest for every training shape. With our third method we present the most generic approach, where only a small set of training landmarks is required. Those landmarks are transferred to the patient speciſc geometry based on Mean Value Coordinates (MVCs). The methods work on surfaces of the pelvis that need to be extracted beforehand. We perform this geometry reconstruction with our previously introduced fully automatic segmentation framework for the pelvic bones. With a focus on the accuracy of our novel MVC-based approach, we evaluate and compare our methods on 100 clinical CT datasets, for which gold standard landmarks were deſned manually by multiple observers.

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Automatic Extraction of Anatomical Landmarks From Medical Image Data: An Evaluation of Different Methods

Note: Research in this publication was not performed at Janelia.

Abstract

Janelia Authors