Classifier-based multi-atlas label propagation with test-specific atlas weighting for correspondence-free scenarios

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8848, Issue , 2014, Pages 116-124

  Zikic, Darko ^a   Glocker, Ben ^b   Criminisi, Antonio ^a  

^a MICROSOFT RESEARCH   (United Kingdom)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER VISION; IMAGE SEGMENTATION; MEDICAL IMAGING; TUMORS;

BRAIN TUMORS; LABEL PROPAGATION; OVER TRAININGS; SEGMENTATION METHODS; TEST IMAGES; TUMOR SEGMENTATION;

BIG DATA;

EID: 84917691009     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-13972-2_11     Document Type: Conference Paper

References (14)

1. Landman, B., Warfield, S. (eds.): MICCAI Workshop on Multi-Atlas Labeling (2012)
2. Asman, A., Akhondi-Asl, A., Wang, H., Tustison, N., Avants, B., Warfield, S.K., Landman, B.: MICCAI 2013 segmentation algorithms, theory and applications (SATA) challenge results summary. In: MICCAI Challenge Workshop on Segmentation: Algorithms, Theory and Applications (SATA) (2013)
3. Wolz, R., Chu, C., Misawa, K., Mori, K., Rueckert, D.: Multi-organ abdominal CT segmentation using hierarchically weighted subject-specific atlases. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 10–17. Springer, Heidelberg (2012)
4. Menze, B., et al.: The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2014). http://hal.inria.fr/hal-00935640
5. Aljabar, P., Heckemann, R., Hammers, A., Hajnal, J., Rueckert, D.: Multi-atlas based segmentation of brain images: Atlas selection and its effect on accuracy. NeuroImage 46(3), 726–738 (2009)
6. Zikic, D., Glocker, B., Criminisi, A.: Atlas encoding by randomized forests for efficient label propagation. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 66–73. Springer, Heidelberg (2013)
7. Akhondi-Asl, A., Warfield, S.: Simultaneous truth and performance level estimation through fusion of probabilistic segmentations. IEEE TMI 32, 1840–1852 (2013)
8. Rohlfing, T., Brandt, R., Menzel, R., Maurer, C.: Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. NeuroImage 21(4), 1428–1442 (2004)
9. Heckemann, R., Hajnal, J., Aljabar, P., Rueckert, D., Hammers, A., et al.: Automatic anatomical brain MRI segmentation combining label propagation and decision fusion. NeuroImage 33(1), 115–126 (2006)
10. Rousseau, F., Habas, P., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE TMI 30(10), 1852–1862 (2011)
11. Coupé, P., Manjón, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.: Patchbased segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940–954 (2011)
12. Warfield, S., Zou, K., Wells, W.: Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE TMI 23(7), 903–921 (2004)
13. Zikic, D., Glocker, B., Konukoglu, E., Shotton, J., Criminisi, A., Ye, D., Demiralp, C., Thomas, O.M., Das, T., Jena, R., Price, S.J.: Context-sensitive classification forests for segmentation of brain tumor tissues. In: MICCAI 2012 Challenge on Multimodal Brain Tumor Segmentation (BraTS) (2012)
14. Tustison, N., Gee, J.: N4ITK: Nick’s N3 ITK implementation for MRI bias field correction. Insight J. (2010)