Detection and location of 127 anatomical landmarks in diverse CT datasets

Mohammad A. Dabbah; Sean Murphy; Hippolyte Pello; Romain Courbon; Erin Beveridge; Stewart Wiseman; Daniel Wyeth; Ian Poole

doi:10.1117/12.2039157

21 March 2014 Detection and location of 127 anatomical landmarks in diverse CT datasets

Mohammad A. Dabbah, Sean Murphy, Hippolyte Pello, Romain Courbon, Erin Beveridge, Stewart Wiseman, Daniel Wyeth, Ian Poole

Author Affiliations +

Proceedings Volume 9034, Medical Imaging 2014: Image Processing; 903415 (2014) https://doi.org/10.1117/12.2039157
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

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,¹ 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.

Citation Download Citation

Mohammad A. Dabbah, Sean Murphy, Hippolyte Pello, Romain Courbon, Erin Beveridge, Stewart Wiseman, Daniel Wyeth, and Ian Poole "Detection and location of 127 anatomical landmarks in diverse CT datasets", Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 903415 (21 March 2014); https://doi.org/10.1117/12.2039157

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available