Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images

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

Volumn 8184 LNCS, Issue , 2013, Pages 1-8

  Kim, Minjeong ^a   Wu, Guorong ^a   Shen, Dinggang ^a  

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

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; MAGNETIC RESONANCE IMAGING; MEDICAL IMAGING;

DISCRIMINATIVE POWER; HIERARCHICAL FEATURES; HIGH-LEVEL FEATURES; HIPPOCAMPUS SEGMENTATION; INDEPENDENT SUBSPACE ANALYSIS; INTENSITY INHOMOGENEITY; LOW-LEVEL FEATURES; STRUCTURAL INFORMATION;

IMAGE SEGMENTATION;

EID: 84886731548     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-02267-3_1     Document Type: Conference Paper

References (15)

1. Zhou, J., Rajapakse, J.C.: Segmentation of subcortical brain structures using fuzzy templates. NeuroImage 28(4), 915-924 (2005)
2. Coupé, P., et al.: Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54(2), 940-954 (2011)
3. Khan, A.R., Wang, L., Beg, M.F.: FreeSurfer-initiated fully-automated subcortical brain segmentation in MRI using Large Deformation Diffeomorphic Metric Mapping. NeuroImage 41(3), 735-746 (2008)
4. Chupin, M., et al.: Automatic segmentation of the hippocampus and the amygdala driven by hybrid constraints: Method and validation. NeuroImage 46(3), 749-761 (2009)
5. van der Lijn, F., et al.: Hippocampus segmentation in MR images using atlas registration, voxel classification, and graph cuts. NeuroImage 43(4), 708-720 (2008)
6. Lötjönen, J.M.P., et al.: Fast and robust multi-atlas segmentation of brain magnetic resonance images. NeuroImage 49(3), 2352-2365
7. Cho, Z.-H., et al.: Quantitative analysis of the hippocampus using images obtained from 7.0 T MRI. NeuroImage 49(3), 2134-2140 (2010)
8. Wang, H., et al.: A learning-based wrapper method to correct systematic errors in automatic image segmentation: Consistently improved performance in hippocampus, cortex and brain segmentation. NeuroImage 55(3), 968-985 (2011)
9. Morra, J.H., Tu, Z., Apostolova, L.G., Green, A.E., Toga, A.W., Thompson, P.M.: Automatic Subcortical Segmentation Using a Contextual Model. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 194-201. Springer, Heidelberg (2008)
10. Tu, Z., Bai, X.: Auto-Context and Its Application to High-Level Vision Tasks and 3D Brain Image Segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1744-1757 (2010)
11. Zhang, S., Zhan, Y., Metaxas, D.N.: Deformable segmentation via sparse representation and dictionary learning. Medical Image Analysis 16(7), 1385-1396 (2012)
12. Zhang, S., et al.: Towards robust and effective shape modeling: Sparse shape composition. Medical Image Analysis 16(1), 265-277 (2012)
13. Bengio, Y.: Learning Deep Architectures for AI. Found. Trends Mach. Learn. 2(1), 1-127 (2009)
14. Shin, H.-C., et al.: Stacked Autoencoders for Unsupervised Feature Learning and Multiple Organ Detection in a Pilot Study Using 4D Patient Data. IEEE Transactions on Pattern Analysis and Machine Intelligence 99(PrePrints), 1 (2012)
15. Le, Q.V., et al.: Learning hierarchical invariant spatio-temporal features for action recognition with independent subspace analysis. In: 2011 IEEE Conference on Computer Vision and Pattern Recognition, CVPR (2011)