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Feature-Space Transformation Improves Supervised Segmentation Across Scanners

  • Annegreet van Opbroek
  • Hakim C. Achterberg
  • Marleen de Bruijne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9487)

Abstract

Image-segmentation techniques based on supervised classification generally perform well on the condition that training and test samples have the same feature distribution. However, if training and test images are acquired with different scanners or scanning parameters, their feature distributions can be very different, which can hurt the performance of such techniques.

We propose a feature-space-transformation method to overcome these differences in feature distributions. Our method learns a mapping of the feature values of training voxels to values observed in images from the test scanner. This transformation is learned from unlabeled images of subjects scanned on both the training scanner and the test scanner.

We evaluated our method on hippocampus segmentation on 27 images of the Harmonized Hippocampal Protocol (HarP), a heterogeneous dataset consisting of 1.5T and 3T MR images. The results showed that our feature space transformation improved the Dice overlap of segmentations obtained with an SVM classifier from 0.36 to 0.85 when only 10 atlases were used and from 0.79 to 0.85 when around 100 atlases were used.

Keywords

Brain Hippocampus Machine learning MRI Transfer learning 

Notes

Acknowledgments

This research is financed by The Netherlands Organization for Scientific Research (NWO).

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Annegreet van Opbroek
    • 1
  • Hakim C. Achterberg
    • 1
  • Marleen de Bruijne
    • 1
    • 2
  1. 1.Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and RadiologyErasmus MC - University Medical Center RotterdamRotterdamThe Netherlands
  2. 2.Department of Computer ScienceUniversity of CopenhagenCopenhagenDenmark

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