A knowledge-guided active model method of skull segmentation on T1-weighted MR images

Zuyao Y. Shan; Chia-Ho Hua; Qing Ji; Carlos Parra; Xiaofei Ying; Matthew J. Krasin; Thomas E. Merchant; Larry E. Kun; Wilburn E. Reddick

doi:10.1117/12.709801

8 March 2007 A knowledge-guided active model method of skull segmentation on T1-weighted MR images

Zuyao Y. Shan, Chia-Ho Hua, Qing Ji, Carlos Parra, Xiaofei Ying, Matthew J. Krasin, Thomas E. Merchant, Larry E. Kun, Wilburn E. Reddick

Author Affiliations +

Proceedings Volume 6512, Medical Imaging 2007: Image Processing; 65122R (2007) https://doi.org/10.1117/12.709801
Event: Medical Imaging, 2007, San Diego, CA, United States

Abstract

Skull is the anatomic landmark for patient set up of head radiation therapy. Skull is generally segmented from CT images because CT provides better definition of skull than MR imaging. In the mean time, radiation therapy is planned on MR images for soft tissue information. This study utilized a knowledge-guided active model (KAM) method to segmented skull on MR images in order to enable radiation therapy planning with MR images as the primary planning dataset. KAM utilized age-specific skull mesh models that segmented from CT images using a conditional region growing algorithm. Skull models were transformed to given MR images using an affine registration algorithm based on normalized mutual information. The transformed mesh models actively located skull boundaries by minimizing their total energy. The preliminary validation was performed on MR and CT images from five patients. The KAM segmented skulls were compared with those segmented from CT images. The average image similarity (kappa index) was 0.57. The initial validation showed that it was promising to segment skulls directly on MR images using KAM.

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Zuyao Y. Shan, Chia-Ho Hua, Qing Ji, Carlos Parra, Xiaofei Ying, Matthew J. Krasin, Thomas E. Merchant, Larry E. Kun, and Wilburn E. Reddick "A knowledge-guided active model method of skull segmentation on T1-weighted MR images", Proc. SPIE 6512, Medical Imaging 2007: Image Processing, 65122R (8 March 2007); https://doi.org/10.1117/12.709801

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