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Joint Segmentation of Multiple Thoracic Organs in CT Images with Two Collaborative Deep Architectures.

Roger Trullo1,2, Caroline Petitjean1, Dong Nie2

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This study introduces a new deep learning framework for automatically segmenting multiple organs at risk (OARs) in thoracic CT scans. The novel approach improves accuracy by considering spatial relationships between organs, crucial for radiotherapy planning.

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Area of Science:

  • Medical Imaging
  • Radiotherapy Planning
  • Deep Learning

Background:

  • Computed Tomography (CT) is essential for radiotherapy planning.
  • Accurate delineation of Organs at Risk (OARs) is critical to protect healthy tissues during radiation therapy.
  • Multi-organ segmentation in thoracic CT is challenging due to low contrast and ill-defined borders.

Purpose of the Study:

  • To develop a novel framework for automatic, collaborative delineation of multiple OARs in thoracic CT images.
  • To address the challenge of low contrast and ill-defined organ borders by incorporating spatial relationships.
  • To improve the accuracy of OAR segmentation for enhanced radiotherapy planning.

Main Methods:

  • Proposed a novel framework with two collaborative deep architectures for joint OAR segmentation.
  • Utilized a deep SharpMask architecture combined with Conditional Random Fields (CRF) to capture low-level and high-level features and spatial relationships.
  • Employed a second deep architecture to refine segmentation using anatomical constraints learned from the first network.

Main Results:

  • The proposed framework demonstrated superior performance in segmenting multiple OARs (esophagus, heart, aorta, trachea) compared to state-of-the-art methods.
  • Achieved improved segmentation accuracy on 30 thoracic CT scans.
  • Effectively leveraged spatial relationships between organs to overcome low contrast challenges.

Conclusions:

  • The novel collaborative deep learning framework offers a significant advancement in automatic OAR delineation for radiotherapy planning.
  • Joint segmentation considering inter-organ spatial relationships enhances accuracy, addressing limitations of single-organ segmentation methods.
  • This approach holds promise for improving the safety and efficacy of radiotherapy treatments.