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March 21, 2026Machine Learning and Knowledge Extraction2 citationsOpen Access

Automated Single-Slice Lumbar QCT HU Value Measurement with Clinical Workflow

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ZYZihao YeJPJun-Mu PengBLBin Lü

Key Points

  • The aim is to develop an automated workflow for accurate measurement of vertebral Hounsfield units (HU) in lumbar QCT scans.
  • Developed a workflow combining unsuitable-slice prescreening and dual-purpose segmentation.
  • Implemented intra-patient slice-quality ranking using PairRank-Swin.
  • Applied a deterministic inner region-of-interest rule for accurate trabecular HU analysis.
  • Conducted a paired HU agreement analysis with 44 evaluable cases.
  • High agreement was observed between automated and manual HU measurements (Pearson’s r = 0.987, Lin’s CCC = 0.985).
  • Mean bias was −0.44 HU with limits of agreement from −14.88 to +13.99 HU.
  • Coverage was 84.1% within ±10 HU and 97.7% within ±15 HU.
  • QC-Envelope segmentation outperformed TotalSegmentator in baseline comparison.

Abstract

Manual single-slice lumbar quantitative computed tomography (QCT) depends on operator-driven slice selection and trabecular region-of-interest (ROI) placement. We developed a fully automated single-slice workflow for vertebral trabecular Hounsfield unit (HU) measurement that combines unsuitable-slice prescreening, dual-purpose segmentation, intra-patient slice-quality ranking, and a deterministic inner ROI rule. The pipeline includes an Eligibility Gate, QC-Envelope segmentation for broad, vertebral- and usability-preserving delineation, PairRank-Swin for best-slice selection, and dedicated trabecular segmentation for final quantitative analysis. In the independent external cohort, 4 cases were considered non-evaluable by both manual review and the pipeline, and 2 additional borderline-quality cases were manually measured but rejected by the pipeline; therefore, paired HU agreement analysis included 44 evaluable cases. Agreement remained high, with Pearson’s r = 0.987, Lin’s CCC = 0.985, mean bias −0.44 HU, and limits of agreement from −14.88 to +13.99 HU. Coverage was 84.1% within ±10 HU and 97.7% within ±15 HU. Ablation analysis showed that slice ranking and ROI erosion were the most critical components. In an open module-level baseline comparison, QC-Envelope segmentation substantially outperformed TotalSegmentator. This workflow provides high agreement with expert HU measurement while preserving reviewable intermediate outputs.

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Cite This Study

Ye et al. (2026) studied this question.

synapsesocial.com/papers/69be37406e48c4981c676bechttps://doi.org/10.3390/make8030077
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