Evaluation of multi atlas-based approaches for the segmentation of the thyroid gland in IMRT head-and-neck CT images

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Volumn 7962, Issue , 2011, Pages

  Chen, Antong ^a   Niermann, Kenneth J ^b   Deeley, Matthew A ^b   Dawant, Benoit M ^a  

^a VANDERBILT UNIVERSITY   (United States)

^b VANDERBILT INGRAM CANCER CENTER   (United States)

Author keywords

atlas based segmentation; head and neck IMRT; nonrigid registration; thyroid gland

Indexed keywords

ATLAS-BASED SEGMENTATION; CT IMAGE; DATA SETS; HEAD AND NECK; HEAD AND NECK IMRT; MULTIPLE SEGMENTATION; NONRIGID REGISTRATION; SEGMENTATION RESULTS; SIMILARITY MEASURE; THYROID GLANDS; TREATMENT PLANNING;

COMPUTERIZED TOMOGRAPHY; IMAGING SYSTEMS; MEDICAL IMAGING;

IMAGE SEGMENTATION;

EID: 79957971158     PISSN: 16057422     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.878246     Document Type: Conference Paper

References (18)

1. Jereczek-Fossa B., Alterio D., Jassem J., et al., Radiotherapy induced thyroid disorders. Cancer Treat Rev, No. 30, 2004. pp. 369-384.
2. Chao, K. S., Bhide, S., Chen, H., et al., Reduce in variation and improve efficiency of target volume delineation by a computer-assisted system using a deformable image registration approach. Int. J Radiat Oncol Biol Phys 68, 2007. pp.1512-1521.
3. Commowick O., Grégoire V., and Malandain G., Atlas-based delineation of lymph node levels in head and neck computed tomography images. Radiother Oncol 87, 2008. pp. 281-289.
4. Commowick O., Warfield S. K., and Malandain G., Using Frankenstein's creature paradigm to build a patient specific atlas. In Proceedings of the 12th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'09), Part II, Vol. 5762, September, 2009. pp. 993-1000.
5. Gorthi, S., Duay, V., Houhou, N., Bach Cuadra, M., Schick, U., Becker, M., Allal, A.S., and Thiran, J.-P. , Segmentation of head and neck lymph node regions for radiotherapy planning using active contour-based atlas registration. IEEE Journal of Selected Topics in Signal Processing, Vol. 3, No. 1, February, 2009. pp. 135-147.
6. Dice, L. R., Measures of the amount of ecologic association between species. Ecology, Vol. 26, No. 3, July 1945. pp. 297-302.
7. Han, X., Hoogeman, M., Levendag, P., et al., Atlas-based auto-segmentation of head and neck CT images. 11th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2008). Vol. 5242 of Lecture Notes in Computer Science, pp. 434-441.
8. Wells W. M., Viola P., Atsumi H., Nakajima S. and Kikinis R., Multi-modal volume registration by maximization of mutual information. Med. Image Anal., 1996, Vol. 1, pp. 35-51.
9. Wu, M., Rosano, C., Lopez-Garcia P., et al., Optimum template selection for atlas-based segmentation. NeuroImage 2007, Vol. 34. pp. 612-1618.
10. Studholme C., Hill D. L. G., and Hawkes D. J., An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition, 1999, Vol. 32, pp. 71-86.
11. Heckemann, R., Hajnal, J., Aljabar, P., et al. Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. Neuroimage 2006, Vol. 33. pp. 115-126.
12. Rohlfing, T., Brandt, R., Menzel, et al., Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 2004, Vol. 21. pp. 1428-1442.
13. Warfield, S., Zou, K., Wells, W., Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Transactions on Medical Imaging 23, 2004. pp. 903-921.
14. Klein, S, van der Heide, U. A., Lips, I. M., van Vulpen, M., Staring, M., and Pluim, J. P., Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Med Phys 2008 Apr. 35(4). pp. 1407-1417.
15. Chen, A., Deeley, M. A., Niermann, K. J., Moretti, L. and Dawant, B. M., Combining registration and active shape models for the automatic segmentation of the lymph node regions in head and neck CT images. Med Phys 2010 Dec. 37(12). pp. 6338-6346.
16. Guimond, A. D., Meunier, J., and Thirion, J. P., Average Brain Models: A Convergence Study. Computer Vision and Image Understanding 77, 2000. pp. 197-201.
17. Rohde, G. K., Aldroubi, A., and Dawant, B. M., The adaptive bases algorithm for intensity-based nonrigid image registration. IEEE Trans. on Medical Imaging, Vol. 22, No. 11, November, 2003. pp. 1470-1479.
18. Noble, J. H., Warren, F. M., Labadie, R. F., and Dawant, B. M., Automatic segmentation of the facial nerve and chorda tympani in CT images using spatially dependent feature values. Med Phys 2008 Dec. 35(12). pp. 5375-5384.