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Abstract <p>Automated multi-organ segmentation looks to assist clinicians and researchers working with Positron Emission Tomography/Computed Tomography (PET/CT) imaging in streamlining the time-consuming, operator-dependent task of manual delineation. This study aimed to compare two state-of-the-art automated multi-organ CT segmentation tools with typically perceived “gold-standard” manual delineation.</p> Methods <p>We compare Multiple-Organ Objective Segmentation (MOOSE) and TotalSegmentator against manual labels of six tissues on a dataset of 24 patients of lung cancer. We evaluated CT segmentation performance using the Dice–Sørensen Coefficient (DSC), Hausdorff Distance (HD), Average Symmetric Surface Distance (ASSD) and pixel-based metrics Precision and Recall. Alongside technical analysis, we perform evaluation using clinically relevant metrics including organ volume, mean standardised uptake value (SUV<sub>mean</sub>), maximum standardised uptake value (SUV<sub>max</sub>), and Hounsfield units.</p> Results <p>Both MOOSE and TotalSegmentator produce overall comparable DSC results. Conversely, MOOSE and TotalSegmentator segmentation results in significantly different volumes and SUVs compared with manual delineation for the lungs, brain, and kidneys.</p> Conclusion <p>Data presented here highlights the need to assess multi-organ segmentation tools performance using multi-pronged metrics beyond Dice-Sørensen scores.</p>

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Comparison of multi-organ CT image segmentation tools for whole-body [18F]FDG-PET/CT clinical imaging

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Cameron Wheeler,
  • Phyo H. Khaing,
  • Eleonora D’Arnese,
  • Adriana A.S. Tavares

摘要

Abstract

Automated multi-organ segmentation looks to assist clinicians and researchers working with Positron Emission Tomography/Computed Tomography (PET/CT) imaging in streamlining the time-consuming, operator-dependent task of manual delineation. This study aimed to compare two state-of-the-art automated multi-organ CT segmentation tools with typically perceived “gold-standard” manual delineation.

Methods

We compare Multiple-Organ Objective Segmentation (MOOSE) and TotalSegmentator against manual labels of six tissues on a dataset of 24 patients of lung cancer. We evaluated CT segmentation performance using the Dice–Sørensen Coefficient (DSC), Hausdorff Distance (HD), Average Symmetric Surface Distance (ASSD) and pixel-based metrics Precision and Recall. Alongside technical analysis, we perform evaluation using clinically relevant metrics including organ volume, mean standardised uptake value (SUVmean), maximum standardised uptake value (SUVmax), and Hounsfield units.

Results

Both MOOSE and TotalSegmentator produce overall comparable DSC results. Conversely, MOOSE and TotalSegmentator segmentation results in significantly different volumes and SUVs compared with manual delineation for the lungs, brain, and kidneys.

Conclusion

Data presented here highlights the need to assess multi-organ segmentation tools performance using multi-pronged metrics beyond Dice-Sørensen scores.