Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models

Minjeong Kim, Guorong Wu, Wei Li, Li Wang, Young Don Son, Zang Hee Cho, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Citations (Scopus)

Abstract

In investigation of neurological diseases, accurate measurement of hippocampus is very important for differentiating inter-subject difference and subtle longitudinal change. Although many automatic segmentation methods have been developed, their performance can be limited by the poor image contrast of hippocampus in the MR images, acquired from either 1.5T or 3.0T scanner. Recently, the emergence of 7.0T scanner sheds new light on the study of hippocampus by providing much higher contrast and resolution. But the automatic segmentation algorithm for 7.0T images still lags behind the development of high-resolution imaging techniques. In this paper, we present a learning-based algorithm for segmenting hippocampi from 7.0T images, by using multi-atlases technique and auto-context models. Specifically, for each atlas (along with other aligned atlases), Auto-Context Model (ACM) is performed to iteratively construct a sequence of classifiers by integrating both image appearance and context features in the local patch. Since there exist plenty of texture information in 7.0T images, more advanced texture features are also extracted and incorporated into the ACM during the training stage. With the use of multiple atlases, multiple sequences of ACM-based classifiers will be trained, respectively in each atlas' space. Thus, in the application stage, a new image will be segmented by first applying the sequence of the learned classifiers of each atlas to it, and then fusing multiple segmentation results from multiple atlases (or multiple sequences of classifiers) by a label-fusion technique. Experimental results on the six 7.0T images with voxel size of 0.35×0.35×0.35mm3 show much better results obtained by our method than by the method using only the conventional auto-context model.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages100-108
Number of pages9
Volume7009 LNCS
DOIs
Publication statusPublished - 2011 Oct 17
Externally publishedYes
Event2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011 - Toronto, ON, Canada
Duration: 2011 Sep 182011 Sep 18

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7009 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011
CountryCanada
CityToronto, ON
Period11/9/1811/9/18

Fingerprint

Hippocampus
Atlas
Classifiers
Classifier
Textures
Segmentation
Scanner
Model
Labels
High Resolution Imaging
Fusion reactions
Texture Feature
Context
Voxel
Imaging techniques
Patch
Texture
Fusion
High Resolution
Model-based

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Kim, M., Wu, G., Li, W., Wang, L., Son, Y. D., Cho, Z. H., & Shen, D. (2011). Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7009 LNCS, pp. 100-108). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS). https://doi.org/10.1007/978-3-642-24319-6_13

Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models. / Kim, Minjeong; Wu, Guorong; Li, Wei; Wang, Li; Son, Young Don; Cho, Zang Hee; Shen, Dinggang.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7009 LNCS 2011. p. 100-108 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7009 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kim, M, Wu, G, Li, W, Wang, L, Son, YD, Cho, ZH & Shen, D 2011, Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7009 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7009 LNCS, pp. 100-108, 2nd International Workshop on Machine Learning in Medical Imaging, MLMI 2011, in Conjunction with the 14th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2011, Toronto, ON, Canada, 11/9/18. https://doi.org/10.1007/978-3-642-24319-6_13
Kim M, Wu G, Li W, Wang L, Son YD, Cho ZH et al. Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7009 LNCS. 2011. p. 100-108. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-24319-6_13
Kim, Minjeong ; Wu, Guorong ; Li, Wei ; Wang, Li ; Son, Young Don ; Cho, Zang Hee ; Shen, Dinggang. / Segmenting hippocampus from 7.0 tesla MR images by combining multiple atlases and auto-context models. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7009 LNCS 2011. pp. 100-108 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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