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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique
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Novel multimodality segmentation using level sets and Jensen-Rényi divergence.

Daniel Markel1, Habib Zaidi, Issam El Naqa

  • 1Medical Physics Unit, University of McGill, Montreal, Quebec H3H 2R9, Canada.

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|December 11, 2013
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Summary
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A new Jensen-Rényi divergence (JRD) algorithm improves positron emission tomography (PET) image segmentation for radiotherapy. This method offers enhanced noise tolerance and multimodal capabilities for image-guided adaptive radiotherapy (IGART).

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Area of Science:

  • Medical Imaging
  • Radiotherapy
  • Image Segmentation

Background:

  • Positron emission tomography (PET) is crucial for radiotherapy planning, but robust segmentation algorithms are lacking, especially for image-guided and adaptive radiotherapy (IGART).
  • Existing methods struggle with noise and multimodal integration, hindering precise tumor delineation.

Purpose of the Study:

  • To present a novel multimodality segmentation algorithm utilizing Jensen-Rényi divergence (JRD) for evolving geometric level set contours.
  • To enhance noise tolerance in PET and cone-beam computed tomography (CT) image segmentation for improved radiotherapy applications.

Main Methods:

  • A steepest gradient ascent optimization method was employed with JRD and a level set active contour.
  • The algorithm iteratively evolves contours based on statistical divergence of intensity histograms.
  • Evaluation involved PET scans of pharyngolaryngeal squamous cell carcinoma, PET/CT scans of lung carcinoma patients, and a physical phantom under varying image quality.

Main Results:

  • JRD segmentation of PET images achieved an average concordance index (CI) of 0.56.
  • Multimodal segmentation of lung carcinoma using PET/CT showed a maximum diameter relative error of 14.8%, significantly lower than CT (63%) or PET (19.5%) alone.
  • Estimated gross tumor volume (GTV) diameters correlated highly with macroscopic measurements (R(2) = 0.88 for PET/CT).
  • JRD demonstrated superior robustness to image noise compared to mutual information and region growing methods in phantom studies.

Conclusions:

  • The JRD algorithm offers improved noise tolerance for PET image segmentation compared to mutual information.
  • A flexible framework for multimodal image segmentation was developed, efficiently incorporating multiple inputs for IGART.
  • The JRD-based approach shows promise for enhancing accuracy and efficiency in image-guided and adaptive radiotherapy.