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Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods

Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods
Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods

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 first method is problem-specific 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 specific 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 defined manually by multiple observers.

Anatomical landmarks, Biomedical measurements, CT, Landmark detection
538-541
IEEE
Seim, Heiko
0285f7e4-f20b-4741-be4d-5db686abf824
Kainmueller, Dagmar
a9962196-0e29-4040-a629-3a1e70722543
Heller, Markus
3da19d2a-f34d-4ff1-8a34-9b5a7e695829
Zachow, Stefan
d3361c36-1a25-4c80-b246-58d9b0a6bdad
Hege, Hans Christian
15f283e0-9d47-43e5-82c8-f787e71eb169
Seim, Heiko
0285f7e4-f20b-4741-be4d-5db686abf824
Kainmueller, Dagmar
a9962196-0e29-4040-a629-3a1e70722543
Heller, Markus
3da19d2a-f34d-4ff1-8a34-9b5a7e695829
Zachow, Stefan
d3361c36-1a25-4c80-b246-58d9b0a6bdad
Hege, Hans Christian
15f283e0-9d47-43e5-82c8-f787e71eb169

Seim, Heiko, Kainmueller, Dagmar, Heller, Markus, Zachow, Stefan and Hege, Hans Christian (2009) Automatic extraction of anatomical landmarks from medical image data: An evaluation of different methods. In Proceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009. IEEE. pp. 538-541 . (doi:10.1109/ISBI.2009.5193103).

Record type: Conference or Workshop Item (Paper)

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 first method is problem-specific 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 specific 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 defined manually by multiple observers.

Full text not available from this repository.

More information

Published date: 2009
Venue - Dates: 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Boston, United States, 2009-06-28 - 2009-07-01
Keywords: Anatomical landmarks, Biomedical measurements, CT, Landmark detection

Identifiers

Local EPrints ID: 415931
URI: http://eprints.soton.ac.uk/id/eprint/415931
PURE UUID: 6a60ffc9-932f-4e02-90ff-802207b015bf
ORCID for Markus Heller: ORCID iD orcid.org/0000-0002-7879-1135

Catalogue record

Date deposited: 28 Nov 2017 17:31
Last modified: 29 Oct 2019 01:39

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