ArticleEuropean journal of nuclear medicine and molecular imaging2026
Comparison of multi-organ CT image segmentation tools for whole-body [
Article in European journal of nuclear medicine and molecular imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- LAFOV PET as an enabling platform for pharmacokinetics-informed digital twins.Frontiers in medicine · 2026Article
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Authors and funding
4 authors.
Funding
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.
methodsWe 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
resultsBoth 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.
conclusionData presented here highlights the need to assess multi-organ segmentation tools performance using multi-pronged metrics beyond Dice-Sørensen scores.
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Registered trials
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