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Expected Label Value Computation for Atlas-Based Image Segmentation.

Iman Aganj1, Bruce Fischl1

  • 1Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology.

Proceedings. IEEE International Symposium on Biomedical Imaging
|July 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for medical image segmentation using expected label values (ELV) instead of computationally expensive deformable registration. This approach avoids local optima and reduces processing time for atlas-based segmentation.

Keywords:
Image segmentationatlasexpected label value (ELV)

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Area of Science:

  • Medical Imaging
  • Computer Vision
  • Computational Anatomy

Background:

  • Multiple atlases are frequently used in medical image segmentation.
  • Deformable registration of atlases to new images is computationally intensive and prone to local optima.

Purpose of the Study:

  • To propose a novel method for atlas-based segmentation that avoids deformable registration.
  • To compute the expected label value (ELV) by considering all possible transformations.

Main Methods:

  • The proposed method calculates the expected label value (ELV) without performing deformable registration.
  • This approach bypasses the computational costs associated with traditional registration methods.

Main Results:

  • The expected label value (ELV) computation was evaluated for liver segmentation.
  • The method was applied to a dataset of computed tomography (CT) images.

Conclusions:

  • The proposed ELV computation offers a computationally efficient alternative to deformable registration for atlas-based segmentation.
  • This method avoids the limitations of traditional registration, such as high computational cost and local optima entrapment.