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Objective <p>To develop and validate an automated, disc-level deep learning pipeline for quantitative measurement of anteroposterior (AP) thecal sac diameter on mid-sagittal lumbar T2-weighted MRI.</p> Materials and methods <p>In this retrospective study, 511 mid-sagittal lumbar T2 MRI examinations were included after screening 758 cases and applying predefined exclusions. The workflow combined YOLOv8 oriented bounding boxes (OBB) for disc-level localization and orientation estimation, homography-based ROI warping, Attention U-Net segmentation, and skeleton-based AP diameter computation in millimeters using DICOM pixel spacing. Validation was performed on internal (50) and external (50; RSNA 2024 lumbar dataset) cohorts with two radiologists providing the reference standard.</p> Result <p>Inter-reader agreement was excellent (ICC (2, 1) = 0.967; 711 paired measurements). Against the reader-mean reference, the pipeline achieved an overall MAE of 0.994&#xa0;mm (711 disc-level measurements). Internal validation showed MAE 0.909&#xa0;mm (357 measurements) and external validation MAE 1.079&#xa0;mm (354 measurements). Severity-wise MAE remained ~ 1&#xa0;mm (mild 0.930&#xa0;mm; moderate 1.234&#xa0;mm; severe 1.038&#xa0;mm). Automatic disc-level labeling was performed, and OBB-derived orientation significantly improved AP measurement-line validity versus axis-aligned detection (acceptable lines 99.02% vs. 77.64%).</p> Conclusion <p>An orientation-aware YOLOv8-OBB + Attention U-Net pipeline enables automated, disc-level AP thecal sac diameter quantification on mid-sagittal lumbar MRI with ~ 1&#xa0;mm error relative to expert reference, supporting standardized morphometric reporting and measurement-driven assessment of lumbar stenosis.</p>

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Automated deep learning pipeline for measuring lumbar thecal sac AP diameter on mid-sagittal MR images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月10日

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  • Akash Nixon,
  • Victor Rakesh Lazar,
  • Saikiran Pendem,
  • Karan Sekar,
  • Senthil Kumar Aiyappan,
  • Sundara Raja Perumal

摘要

Objective

To develop and validate an automated, disc-level deep learning pipeline for quantitative measurement of anteroposterior (AP) thecal sac diameter on mid-sagittal lumbar T2-weighted MRI.

Materials and methods

In this retrospective study, 511 mid-sagittal lumbar T2 MRI examinations were included after screening 758 cases and applying predefined exclusions. The workflow combined YOLOv8 oriented bounding boxes (OBB) for disc-level localization and orientation estimation, homography-based ROI warping, Attention U-Net segmentation, and skeleton-based AP diameter computation in millimeters using DICOM pixel spacing. Validation was performed on internal (50) and external (50; RSNA 2024 lumbar dataset) cohorts with two radiologists providing the reference standard.

Result

Inter-reader agreement was excellent (ICC (2, 1) = 0.967; 711 paired measurements). Against the reader-mean reference, the pipeline achieved an overall MAE of 0.994 mm (711 disc-level measurements). Internal validation showed MAE 0.909 mm (357 measurements) and external validation MAE 1.079 mm (354 measurements). Severity-wise MAE remained ~ 1 mm (mild 0.930 mm; moderate 1.234 mm; severe 1.038 mm). Automatic disc-level labeling was performed, and OBB-derived orientation significantly improved AP measurement-line validity versus axis-aligned detection (acceptable lines 99.02% vs. 77.64%).

Conclusion

An orientation-aware YOLOv8-OBB + Attention U-Net pipeline enables automated, disc-level AP thecal sac diameter quantification on mid-sagittal lumbar MRI with ~ 1 mm error relative to expert reference, supporting standardized morphometric reporting and measurement-driven assessment of lumbar stenosis.