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Unsupervised Deep Learning for Hippocampus Segmentation in 7.0 Tesla MR Images

  • Minjeong Kim
  • Guorong Wu
  • Dinggang Shen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8184)

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.

Keywords

Image Patch Basis Filter Hierarchical Feature Segmentation Framework Haar Feature 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  • Minjeong Kim
    • 1
  • Guorong Wu
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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