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Exploring Automated Contouring Across Institutional Boundaries: A Deep Learning Approach with Mouse Micro-CT

Lu Jiang1, Di Xu1, Qifan Xu1

  • 1Department of Radiation Oncology, University of California San Francisco.

Arxiv
|June 10, 2024
PubMed
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This summary is machine-generated.

Swin UNEt Transformers (Swin UNETR) accurately segment mouse organs in micro-CT scans, outperforming other models. This advanced AI tool enhances pre-clinical research by providing robust and generalizable automated contouring for image-guided irradiation studies.

Area of Science:

  • Medical imaging
  • Artificial intelligence in preclinical research
  • Radiotherapy research

Background:

  • Accurate organ segmentation in mouse models is crucial for preclinical radiation studies.
  • Current segmentation methods may lack robustness across different imaging conditions.

Purpose of the Study:

  • To evaluate Swin UNEt Transformers (Swin UNETR) for segmenting mouse organs in micro-computed tomography (micro-CT) scans.
  • To benchmark Swin UNETR against 3D no-new-Net (nnU-Net) and other models.
  • To assess the robustness and generalizability of Swin UNETR on external datasets.

Main Methods:

  • Swin UNETR was employed for sequence-to-sequence organ segmentation.
  • A hierarchical Swin Transformer encoder and a Fully Convolutional Neural Network (FCNN) decoder with skip connections were utilized.
Keywords:
Swin Transformersdeep learningmicro-CTmouseorgan segmentation

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  • Models were trained and validated on open datasets, with further testing on an external dataset with varying imaging parameters.

    • Main Results:

    • Swin UNETR demonstrated superior performance over nnU-Net and AIMOS in average Dice Similarity Coefficient (DSC) and Hausdorff Distance (HD95p).
    • The model showed exceptional robustness and generalizability on an external dataset with lower image quality and higher noise.
    • Minor limitations were observed in intestine contouring for specific cases.

    Conclusions:

    • Swin UNETR is a highly generalizable and efficient tool for automated organ contouring in pre-clinical workflows.
    • The model's performance indicates its suitability for image-guided mouse irradiation studies.
    • Swin UNETR offers improved accuracy and reliability compared to existing methods, especially under challenging imaging conditions.