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Patch-Based Label Fusion for Automatic Multi-Atlas-Based Prostate Segmentation in MR Images.

Xiaofeng Yang1, Ashesh B Jani1, Peter J Rossi1

  • 1Department of Radiation Oncology and Winship Cancer Institute.

Proceedings of Spie--The International Society for Optical Engineering
|August 28, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel 3D multi-atlas segmentation method for prostate MR images using patch-based label fusion. The approach accurately segments prostates, showing clinical feasibility and high accuracy compared to manual segmentations.

Keywords:
MRIProstate segmentationlabel fusionmulti-atlasprostate cancer

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Area of Science:

  • Medical Imaging
  • Computer-Aided Diagnosis
  • Biomedical Engineering

Background:

  • Accurate prostate segmentation in MRI is crucial for diagnosis and treatment planning.
  • Existing segmentation methods face challenges with image variability and anatomical complexity.

Purpose of the Study:

  • To develop and validate a 3D multi-atlas-based prostate segmentation method for MR images.
  • To improve the accuracy and efficiency of automated prostate segmentation.

Main Methods:

  • A novel patch-based label fusion strategy was employed, selecting atlases with similar appearances.
  • Local patch-based atlas fusion utilized voxel weighting based on anatomical signatures.
  • The method was validated on a clinical dataset of 13 patients, comparing results to physician manual segmentations (gold standard).

Main Results:

  • The proposed method demonstrated accurate prostate segmentation in MR images.
  • Dice volumetric overlap was used to quantify segmentation accuracy.
  • The technique showed clinical feasibility and validated accuracy against manual segmentations.

Conclusions:

  • A new non-local patch-based label fusion approach for prostate MR segmentation was successfully developed.
  • The method is clinically feasible and achieves high accuracy validated by manual segmentations.
  • This technique offers a promising tool for automated prostate segmentation in clinical practice.