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Assessing Quantitative Performance and Expert Review of Multiple Deep Learning-Based Frameworks for Computed

Udbhav S Ram1, Joel A Pogue1, Michael Soike1

  • 1Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Alabama, 35233, United States.

Intelligent Oncology
|September 29, 2025
PubMed
Summary

Automated Machine Learning (AutoML) frameworks significantly improve abdominal organ segmentation in CT scans for oncology. These tools enhance efficiency and accuracy compared to traditional methods, aiding treatment planning.

Keywords:
Auto3DSegAutoMLRadiation OncologySegmentationnnU-Net

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Oncology
  • Computational Pathology

Background:

  • Accurate segmentation of abdominal organs in CT images is vital for oncological treatment planning and follow-up.
  • Manual segmentation is time-consuming, labor-intensive, and prone to inter-observer variability.
  • Deep learning (DL) and Automated Machine Learning (AutoML) offer promising solutions for automated segmentation.

Purpose of the Study:

  • To comprehensively evaluate the performance of AutoML frameworks (Auto3DSeg, nnU-Net) against a state-of-the-art non-AutoML framework (SwinUNETR).
  • To compare quantitative metrics and qualitative clinical viability of different segmentation frameworks.

Main Methods:

  • Frameworks were trained on 122 Abdominal Multi-Organ Segmentation (AMOS) challenge images.
  • Performance was assessed using Dice Similarity Coefficient (DSC), Surface DSC (sDSC), and 95th Percentile Hausdorff Distances (HD95) on 72 validation images.
  • Clinical viability of 30 auto-contoured cases was evaluated by three physicians in a blinded assessment.

Main Results:

  • AutoML frameworks demonstrated superior performance: nnU-Net (DSC: 0.924, sDSC: 0.938, HD95: 4.26) and Auto3DSeg (DSC: 0.902, sDSC: 0.919, HD95: 8.76).
  • SwinUNETR showed lower performance (DSC: 0.837, sDSC: 0.844, HD95: 13.93).
  • AutoML methods were quantitatively preferred, with nnU-Net also showing qualitative preference over Auto3DSeg.

Conclusions:

  • AutoML frameworks provide a significant advantage for abdominal organ segmentation in CT images.
  • These findings highlight the potential of AutoML to enhance efficiency and accuracy in oncological workflows.