What question did this study set out to answer?

To develop an automated pipeline for measuring lumbar thecal sac diameter using mid-sagittal MR images.

February 13, 2026BMC Medical Imaging0 citationsOpen Access

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

ANAkash NixonSRM Institute of Science and Technology VLVictor Rakesh LazarSRM Institute of Science and Technology SPSaikiran PendemManipal Academy of Higher Education

Key Points

To develop an automated pipeline for measuring lumbar thecal sac diameter using mid-sagittal MR images.
Utilized a YOLOv8-OBB model for detection of anatomical structures.
Incorporated Attention U-Net for enhancing image segmentation.
Employed mid-sagittal MR images for analysis.
Achieved approximately 1 mm measurement error compared to expert references.
Supported reliable and standardized morphometric reporting.
Enabled assessment of lumbar stenosis through automated measures.

Abstract

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.

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