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Anisotropic 3D Multi-Stream CNN for Accurate Prostate Segmentation from Multi-Planar MRI.

Anneke Meyer1, Grzegorz Chlebus2, Marko Rak1

  • 1Faculty of Computer Science and Research Campus STIMULATE, University of Magdeburg, Germany.

Computer Methods and Programs in Biomedicine
|November 21, 2020
PubMed
Summary

Utilizing multi-planar magnetic resonance imaging (MRI) data improves prostate cancer segmentation accuracy. This enhanced segmentation aids in precise treatment planning and monitoring for better patient outcomes.

Keywords:
Anisotropic CNNHyperparameter OptimizationMRIMulti-Stream-CNNProstate Segmentation

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Area of Science:

  • Medical imaging analysis
  • Artificial intelligence in healthcare
  • Oncology research

Background:

  • Prostate cancer diagnosis and treatment rely on accurate prostate gland segmentation in MR images.
  • Current segmentation methods often use only axial scans, neglecting valuable multi-planar information.
  • Standardized MRI protocols provide multi-planar data crucial for comprehensive analysis.

Purpose of the Study:

  • To investigate the efficacy of neural networks processing anisotropic multi-planar MR images for prostate gland segmentation.
  • To determine if incorporating additional scan directions improves segmentation quality compared to axial-only approaches.
  • To evaluate the impact of multi-planar information on semantic segmentation tasks in prostate MRI.

Main Methods:

  • Development of an anisotropic 3D multi-stream Convolutional Neural Network (CNN) architecture.
  • Processing of dual-plane (two orientations) and triple-plane (three orientations) MR images.
  • Comparison of multi-planar models against a single-plane (axial-only) baseline using hyperparameter optimization.

Main Results:

  • Statistically significant improvements in prostate segmentation were observed using multi-planar data (p<0.05 Dice similarity coefficient).
  • Triple-plane segmentation showed improvement at the prostate base (0.906 vs. 0.898).
  • Dual-plane segmentation demonstrated enhancement at the prostate apex (0.901 vs. 0.888).

Conclusions:

  • Multi-planar segmentation models (dual-plane and triple-plane) outperform traditional axial-only methods.
  • Accurate prostate boundary delineation is vital for preserving critical structures during therapy.
  • The proposed models offer potential to enhance prostate cancer diagnosis and therapeutic interventions.