ArticleBMC medical imaging2026
Automated deep learning pipeline for measuring lumbar thecal sac AP diameter on mid-sagittal MR images.
Article in BMC medical imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo 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
methodsIn 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.
resultInter-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%).
conclusionAn 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.