Scholarlink

<p>This study presents the validation of a fully automated, annotation-free CT pelvimetry pipeline that is built on a publicly available, interchangeable segmentation backend (here, TotalSegmentator) without task-specific retraining, derives seven pelvimetric parameters through deterministic rule-based landmark extraction, and is released as an open-source package. Technical validation was performed in 60 patients by comparison with repeated manual measurements from two independent measurement sessions using intraclass correlation coefficients (ICC), Bland–Altman analysis, and three-dimensional landmark displacement quantification. Exploratory clinical evaluation was conducted in a single-surgeon cohort (<i>n</i> = 106). The automated pipeline successfully processed 199 of 200 consecutive CT datasets (99.5%). Automated measurements demonstrated moderate-to-excellent agreement with manual pelvimetry (ICC 0.66–0.97). Metrics defined by well-corticated landmarks showed the highest agreement (interspinous-distance ICC, 0.97), whereas measurements dependent on the coccygeal apex exhibited greater variability (mean 3D displacement 5.4&#xa0;mm intra-rater, 8.9&#xa0;mm automated-versus-manual). Coordinate-level analysis traced measurement variability predominantly to specific anatomically ambiguous landmarks, providing a diagnostic capability absent from conventional aggregate validation approaches. In exploratory clinical evaluation, wider transverse pelvic dimensions were associated with lower intraoperative blood loss. The open-source implementation enables scalable, annotation-free pelvimetric assessment with coordinate-level uncertainty characterization potentially applicable to other domains where geometric parameters are derived from segmentation masks.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Scalable, Annotation-Free Pipeline for Automated CT Pelvimetry: A Validation Study with Landmark Uncertainty Analysis and Clinical Correlation

  • 期刊论文

  • 发表日期：

    2026年7月29日

查看全文
  • Shih-Feng Huang,
  • Hsin-Ping Tseng,
  • Yu-Hsun Chen,
  • Yung-Lin Tan,
  • Jen-Ping Lee,
  • Chih-Chien Wu,
  • Chao-Wen Hsu

摘要

This study presents the validation of a fully automated, annotation-free CT pelvimetry pipeline that is built on a publicly available, interchangeable segmentation backend (here, TotalSegmentator) without task-specific retraining, derives seven pelvimetric parameters through deterministic rule-based landmark extraction, and is released as an open-source package. Technical validation was performed in 60 patients by comparison with repeated manual measurements from two independent measurement sessions using intraclass correlation coefficients (ICC), Bland–Altman analysis, and three-dimensional landmark displacement quantification. Exploratory clinical evaluation was conducted in a single-surgeon cohort (n = 106). The automated pipeline successfully processed 199 of 200 consecutive CT datasets (99.5%). Automated measurements demonstrated moderate-to-excellent agreement with manual pelvimetry (ICC 0.66–0.97). Metrics defined by well-corticated landmarks showed the highest agreement (interspinous-distance ICC, 0.97), whereas measurements dependent on the coccygeal apex exhibited greater variability (mean 3D displacement 5.4 mm intra-rater, 8.9 mm automated-versus-manual). Coordinate-level analysis traced measurement variability predominantly to specific anatomically ambiguous landmarks, providing a diagnostic capability absent from conventional aggregate validation approaches. In exploratory clinical evaluation, wider transverse pelvic dimensions were associated with lower intraoperative blood loss. The open-source implementation enables scalable, annotation-free pelvimetric assessment with coordinate-level uncertainty characterization potentially applicable to other domains where geometric parameters are derived from segmentation masks.