Representation learning: A unified deep learning framework for automatic prostate MR segmentation

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 254-261

  Liao, Shu ^a   Gao, Yaozong ^a   Oto, Aytekin ^b   Shen, Dinggang ^a  

^a UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^b UNIVERSITY OF CHICAGO   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL INFORMATION; FEATURE ENGINEERINGS; HIGH LEVEL SEMANTICS; HISTOGRAM OF ORIENTED GRADIENTS; IMAGE REPRESENTATIONS; INDEPENDENT SUBSPACE ANALYSIS; LOCAL BINARY PATTERNS; SEGMENTATION ACCURACY;

IMAGE ANALYSIS; IMAGE SEGMENTATION; SEMANTICS;

UROLOGY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HUMAN; IMAGE ENHANCEMENT; MALE; METHODOLOGY; NUCLEAR MAGNETIC RESONANCE IMAGING; PATHOLOGY; PROSTATE TUMOR; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84897576138     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_32     Document Type: Conference Paper

References (14)

1. Shen, D., Davatzikos, C.: HAMMER: Hierarchical attribute matching mechanism for elastic registration. IEEE TMI 21, 1421-1439 (2002)
2. Guo, Y., Zhao, G., Pietikainen, M.: Discriminative features for texture description. Pattern Recognition 45, 3834-3843 (2012)
3. Mallat, G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE PAMI 11, 674-693 (1989)
4. Dalal, N., Triggs, B.: Histograms of oriented gradients for human detection. In: CVPR, pp. 886-893 (2005)
5. Bengio, Y., Lamblin, P., Popovici, D., Larochelle, H.: Greedy layerwise training of deep networks. In: NIPS, pp. 153-160 (2006)
6. Hinton, G., Osindero, S., Teh, Y.: A fast learning algorithm for deep belief nets. Neu. Comp., 1527-1554 (2006)
7. Le, Q., Zou, W., Yeung, S., Ng, A.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: CVPR, pp. 3361-3368 (2011)
8. Hyvarinen, A., Hurri, J., Hoyer, P.: Natural Image Statistics. Springer (2009)
9. Rousseau, F., Habas, P., Studholme, C.: A supervised patch-based approach for human brain labeling. IEEE TMI 30, 1852-1862 (2011)
10. Coupe, P., Manjon, J., Fonov, V., Pruessner, J., Robles, M., Collins, D.: Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. NeuroImage 54, 940-954 (2011)
11. Zhang, S., Zhan, Y., Dewan, M., Huang, J., Metaxas, D., Zhou, X.: Towards robust and effective shape modeling: sparse shape composition. MedIA 16, 265-277 (2012)
12. Klein, S., Heide, U., Lips, I., Vulpen, M., Staring, M., Pluim, J.: Automatic segmentation of the prostate in 3D MR images by atlas matching using localized mutual information. Medical Physics 35, 1407-1417 (2008)
13. Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE PAMI 24, 971-987 (2002)
14. Liao, S., Gao, Y., Shen, D.: Sparse patch based prostate segmentation in CT images. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 385-392. Springer, Heidelberg (2012)