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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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Sparse patch based prostate segmentation in CT images.

Shu Liao1, Yaozong Gao, Dinggang Shen

  • 1Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA. liaoshu.cse@gmail.com

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|January 5, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a novel patient-specific method for accurate prostate segmentation in CT images, crucial for image-guided radiation therapy. The approach enhances segmentation accuracy by addressing low contrast, motion, and image variations.

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Computer-Aided Diagnosis

Background:

  • Accurate prostate segmentation is vital for image-guided radiation therapy.
  • Challenges include low contrast, prostate motion, and bowel gas artifacts in CT images.

Purpose of the Study:

  • To develop a patient-specific method for robust prostate segmentation in CT images.
  • To overcome limitations of existing methods in handling image variations and motion.

Main Methods:

  • A novel patch-based representation in a discriminative feature space for voxel distinction.
  • Integration with a sparse label propagation framework to refine segmentation.
  • An online update mechanism to incorporate patient-specific information from prior treatment scans.

Main Results:

  • The method was evaluated on a dataset of 330 CT images from 24 patients.
  • Demonstrated superior segmentation accuracy compared to state-of-the-art approaches.
  • Effectively addressed challenges of low contrast, motion, and image appearance variations.

Conclusions:

  • The proposed patient-specific method significantly improves prostate segmentation accuracy in CT images.
  • Offers a more reliable solution for image-guided radiation therapy planning.
  • Highlights the potential of patch-based representations and sparse label propagation for medical image segmentation.