Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images

Minjeong Kim, Guorong Wu, Dinggang Shen

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

14 Citations (Scopus)

Abstract

Recent emergence of 7.0T MR scanner sheds new light on the study of hippocampus by providing much higher image contrast and resolution. However, the new characteristics shown in 7.0T images, such as richer structural information and more severe intensity inhomogeneity, raise serious issues for the extraction of distinctive and robust features for accurately segmenting hippocampus in 7.0T images. On the other hand, the hand-crafted image features (such as Haar and SIFT), which were designed for 1.5T and 3.0T images, generally fail to be effective, because of the considerable image artifacts in 7.0T images. In this paper, we introduce the concept of unsupervised deep learning to learn the hierarchical feature representation directly from the pre-observed image patches in 7.0T images. Specifically, a two-layer stacked convolutional Independent Subspace Analysis (ISA) network is built to learn not only the intrinsic low-level features from image patches in the lower layer, but also the high-level features in the higher layer to describe the global image appearance based on the outputs from the lower layer. We have successfully integrated this deep learning scheme into a state-of-the-art multi-atlases based segmentation framework by replacing the previous hand-crafted image features by the hierarchical feature representations inferred from the two-layer ISA network. Promising hippocampus segmentation results were obtained on 20 7.0T images, demonstrating the enhanced discriminative power achieved by our deep learning method.

Original languageEnglish
Title of host publicationMachine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings
PublisherSpringer Verlag
Pages1-8
Number of pages8
ISBN (Print)9783319022666
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013 - Nagoya, Japan
Duration: 2013 Sep 222013 Sep 22

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8184 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Workshop on Machine Learning in Medical Imaging, MLMI 2013, Held in Conjunction with 16th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2013
CountryJapan
CityNagoya
Period13/9/2213/9/22

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images'. Together they form a unique fingerprint.

  • Cite this

    Kim, M., Wu, G., & Shen, D. (2013). Unsupervised deep learning for hippocampus segmentation in 7.0 Tesla MR images. In Machine Learning in Medical Imaging - 4th International Workshop, MLMI 2013, Held in Conjunction with MICCAI 2013, Proceedings (pp. 1-8). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8184 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-02267-3_1