Uncertainty-driven forest predictors for vertebra localization and segmentation

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

Volumn 9349, Issue , 2015, Pages 653-660

  Richmond, David ^a   Kainmueller, Dagmar ^a   Glocker, Ben ^b   Rother, Carsten ^c   Myers, Gene ^a  

^a MAX PLANCK INSTITUTE OF MOLECULAR CELL BIOLOGY AND GENETICS   (Germany)

^b IMPERIAL COLLEGE LONDON   (United Kingdom)

^c DRESDEN UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTERIZED TOMOGRAPHY; DECISION TREES; IMAGE PROCESSING; MEDICAL IMAGING; MUSCULOSKELETAL SYSTEM; SEMANTICS;

BIOLOGICAL IMAGE ANALYSIS; CONSTELLATION MODEL; FEATURE GENERATION; LOCALIZATION AND IDENTIFICATION; MICROSCOPY IMAGES; SEMANTIC SEGMENTATION; TWO-STAGE APPROACHES; ZEBRAFISH EMBRYOS;

IMAGE SEGMENTATION;

EID: 84947610439     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-24553-9_80     Document Type: Chapter

References (16)

1. Glocker, B., Feulner, J., Criminisi, A., Haynor, D.R., Konukoglu, E.: Automatic localization and identification of vertebrae in arbitrary field-of-view CT scans. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 590–598. Springer, Heidelberg (2012)
2. Glocker, B., Zikic, D., Konukoglu, E., Haynor, D.R., Criminisi, A.: Vertebrae localization in pathological spine CT via dense classification from sparse annotations. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 262–270. Springer, Heidelberg (2013)
3. Seifert, S., Barbu, A., Zhou, S.K., Liu, D., Feulner, J., Huber, M., Suehling, M., Cavallaro, A., Comaniciu, D.: Hierarchical parsing and semantic navigation of full body CT data. In: SPIE Medical Imaging 2009: Image Processing, vol. 7259, pp. 725902–725902–8 (2009)
4. Shotton, J., Winn, J.M., Rother, C., Criminisi, A.: textonBoost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation. In: Leonardis, A., Bischof, H., Pinz, A. (eds.) ECCV 2006, Part I. LNCS, vol. 3951, pp. 1–15. Springer, Heidelberg (2006)
5. Schroff, F., Criminisi, A., Zisserman, A.: Object class segmentation using random forests. In: BMVC (2008)
6. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Pereira, F., Burges, C., Bottou, L., Weinberger, K. (eds.) NIPS, pp. 1097–1105. Curran Associates, Inc. (2012)
7. Tu, Z.: Auto-context and its application to high-level vision tasks. In: CVPR 2008, pp. 1–8 (June 2008)
8. Nowozin, S., Rother, C., Bagon, S., Sharp, T., Yao, B., Kohli, P.: Decision tree fields. In: ICCV, pp. 1668–1675 (2011)
9. Schmidt, U., Rother, C., Nowozin, S., Jancsary, J., Roth, S.: Discriminative nonblind deblurring. In: CVPR, pp. 604–611 (2013)
10. Kontschieder, P., Kohli, P., Shotton, J., Criminisi, A.: Geof: Geodesic forests for learning coupled predictors. In: CVPR, pp. 65–72 (2013)
11. Yao, J., Glocker, B., Klinder, T., Li, S.: Recent advances in computational methods and clinical applications for spine imaging (2015)
12. Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)
13. Roberts, M.G., Cootes, T.F., Adams, J.E.: Automatic location of vertebrae on dxa images using random forest regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 361–368. Springer, Heidelberg (2012)
14. Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(6), 681–685 (2001)
15. Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE TPAMI 28(10), 1568–1583 (2006)
16. Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society. Series B (Methodological), 259–302 (1986)