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Automatic segmentation of the pelvic bones from CT data based on a statistical shape model

Automatic segmentation of the pelvic bones from CT data based on a statistical shape model
Automatic segmentation of the pelvic bones from CT data based on a statistical shape model

We present an algorithm for automatic segmentation of the human pelvic bones from CT datasets that is based on the application of a statistical shape model. The proposed method is divided into three steps: 1) The averaged shape of the pelvis model is initially placed within the CT data using the Generalized Hough Transform, 2) the statistical shape model is then adapted to the image data by a transformation and variation of its shape modes, and 3) a final free-form deformation step based on optimal graph searching is applied to overcome the restrictive character of the statistical shape representation. We thoroughly evaluated the method on 50 manually segmented CT datasets by performing a leave-one-out study. The Generalized Hough Transform proved to be a reliable method for an automatic initial placement of the shape model within the CT data. Compared to the manual gold standard segmentations, our automatic segmentation approach produced an average surface distance of 1.2 ± 0.3mm after the adaptation of the statistical shape model, which could be reduced to 0.7±0.3mm using a final free-form deformation step. Together with an average segmentation time of less than 5 minutes, the results of our study indicate that our method meets the requirements of clinical routine.

93-100
Eurographics Association
Seim, H.
859fdcd8-ecb0-439d-a308-fd19f2162c06
Kainmueller, D.
e1782a58-bd78-4d43-99e6-10efa3e163a7
Heller, M.
3da19d2a-f34d-4ff1-8a34-9b5a7e695829
Lamecker, H.
cd086054-1d56-43ff-b1f4-ee3b381bf745
Zachow, S.
457f75b7-a693-4776-8c50-40f20cb58af0
Hege, H.C.
3bf725d4-0f21-4d42-9521-3957f731931f
Seim, H.
859fdcd8-ecb0-439d-a308-fd19f2162c06
Kainmueller, D.
e1782a58-bd78-4d43-99e6-10efa3e163a7
Heller, M.
3da19d2a-f34d-4ff1-8a34-9b5a7e695829
Lamecker, H.
cd086054-1d56-43ff-b1f4-ee3b381bf745
Zachow, S.
457f75b7-a693-4776-8c50-40f20cb58af0
Hege, H.C.
3bf725d4-0f21-4d42-9521-3957f731931f

Seim, H., Kainmueller, D., Heller, M., Lamecker, H., Zachow, S. and Hege, H.C. (2008) Automatic segmentation of the pelvic bones from CT data based on a statistical shape model. In EG VCBM 2008 - Eurographics Workshop on Visual Computing for Biomedicine. Eurographics Association. pp. 93-100 .

Record type: Conference or Workshop Item (Paper)

Abstract

We present an algorithm for automatic segmentation of the human pelvic bones from CT datasets that is based on the application of a statistical shape model. The proposed method is divided into three steps: 1) The averaged shape of the pelvis model is initially placed within the CT data using the Generalized Hough Transform, 2) the statistical shape model is then adapted to the image data by a transformation and variation of its shape modes, and 3) a final free-form deformation step based on optimal graph searching is applied to overcome the restrictive character of the statistical shape representation. We thoroughly evaluated the method on 50 manually segmented CT datasets by performing a leave-one-out study. The Generalized Hough Transform proved to be a reliable method for an automatic initial placement of the shape model within the CT data. Compared to the manual gold standard segmentations, our automatic segmentation approach produced an average surface distance of 1.2 ± 0.3mm after the adaptation of the statistical shape model, which could be reduced to 0.7±0.3mm using a final free-form deformation step. Together with an average segmentation time of less than 5 minutes, the results of our study indicate that our method meets the requirements of clinical routine.

Full text not available from this repository.

More information

Published date: 2008
Venue - Dates: 1st Eurographics Workshop on Visual Computing and Biomedicine, EG VCBM 2008, Delft, Netherlands, 2008-10-06 - 2008-10-07

Identifiers

Local EPrints ID: 415934
URI: http://eprints.soton.ac.uk/id/eprint/415934
PURE UUID: 676aeec0-9dfe-496b-bedc-fab0650cf993
ORCID for M. Heller: ORCID iD orcid.org/0000-0002-7879-1135

Catalogue record

Date deposited: 28 Nov 2017 17:31
Last modified: 17 Dec 2019 01:38

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