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Automated brain structures segmentation from PET/CT images based on landmark-constrained dual-modality atlas

Zhaofeng Chen1,2, Tianshuang Qiu1, Yang Tian1

  • 1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024, People's Republic of China.

Physics in Medicine and Biology
|March 25, 2021
PubMed
Summary

This study introduces an automated algorithm for segmenting brain structures in PET/CT scans, improving accuracy for deep brain regions. The method enhances diagnosis and follow-up for brain diseases by optimizing both global and local alignment.

Keywords:
anatomical landmarkatlas registrationbrain atlasbrain structure segmentationpositron emission tomography (PET)

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Radiology

Background:

  • Automated segmentation of brain structures in PET images aids in diagnosing and monitoring brain diseases.
  • Existing atlas-based methods often struggle with accuracy for small, deep brain structures due to global optimization strategies.

Purpose of the Study:

  • To develop a novel PET/CT-based algorithm for accurate brain volume of interest (VOI) segmentation.
  • To improve the precision of atlas registration for deep brain structures using local landmarks and dual-modality information.

Main Methods:

  • Combines anatomical atlases with local deep brain landmarks detected via Deep Q-Network (DQN).
  • Integrates dual-modality PET/CT information to refine extracerebral contour registration.
  • Constrains atlas registration using detected local landmarks for improved accuracy.

Main Results:

  • Achieved high accuracy in brain VOI delineation on 86 clinical PET/CT images.
  • Reported an average Dice similarity score of 0.79 and an average surface distance of 0.97 mm.
  • Demonstrated a volume recovery coefficient close to 1, indicating precise volume measurement.

Conclusions:

  • The proposed algorithm effectively optimizes global brain matching and local structure alignment.
  • It offers a fully automated solution for high-quality brain structure parcellation from PET/CT images.
  • This method enhances the accuracy of automated brain segmentation for clinical applications.