Automated quantitative computed tomography grading of lumbar facet joint degeneration: influence of segmentation errors on instance-level clinical assessment.
- 1Department of Orthopedics, Affiliated Nantong Clinical College of Nantong University, Nantong First People's Hospital, School of Medicine, Nantong University, Nantong, China.
- 2Department of Orthopedics, Nantong First People's Hospital, Southeast University, Nantong, China.
Abstract
Automated lumbar facet joint degeneration grading requires both accurate anatomical segmentation and clinically meaningful region-level classification. We evaluated whether voxel-level segmentation performance was aligned with downstream degeneration grading. This retrospective study included 145 lumbar computed tomography (CT) examinations and 725 level-specific bilateral facet joint regions of interest (ROIs), including 165 severe and 560 non-severe regions. Weishaupt grades 0-1 were categorized as non-severe and grades 2-3 as severe. Scheme A used binary nnU-Net segmentation followed by 11 quantitative region-level features, training-fold-specific standardization, and logistic regression; an oracle comparison using ground-truth masks was compared with analysis using predicted masks. Scheme B used direct three-class segmentation. Patient-level five-fold cross-validation was performed, with 116 training and 29 validation patients per fold. The results were reported as mean ± standard deviation (SD). Scheme A achieved a binary segmentation Dice coefficient of 0.770±0.008. In the oracle comparison, the region-level area under the receiver operating characteristic (ROC) curve (AUC), recall, F1-score, accuracy, and case-level accuracy were 0.671±0.100, 0.538±0.172, 0.399±0.102, 0.640±0.051, and 0.579±0.096, respectively. Using predicted masks, the corresponding values were 0.650±0.068, 0.603±0.250, 0.375±0.072, 0.563±0.123, and 0.545±0.071. Scheme B achieved a native nnU-Net mean validation Dice of 0.493±0.044 and region-level AUC, recall, F1-score, accuracy, and case-level accuracy of 0.730±0.024, 0.445±0.051, 0.510±0.045, 0.808±0.015, and 0.738±0.058, respectively. Voxel-level segmentation and clinically oriented grading performance were not directly aligned. Scheme A provided stable anatomical segmentation but more variable downstream grading, whereas scheme B achieved better overall region- and case-level classification performance but lower severe-degeneration recall. Both spatial and clinical decision metrics should therefore be considered when evaluating automated facet joint grading systems.
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BibTeX
@article{chen2026automated,
title = {{Automated quantitative computed tomography grading of lumbar facet joint degeneration: influence of segmentation errors on instance-level clinical assessment.}},
author = {Baisen Chen and Yukang Cheng and Chunyan Ji and Zhiming Cui and Guanhua Xu and Yuyu Sun},
journal = {Quantitative imaging in medicine and surgery},
year = {2026},
month = oct,
doi = {10.21037/qims-2026-1485},
url = {https://europepmc.org/article/MED/42997901},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - Automated quantitative computed tomography grading of lumbar facet joint degeneration: influence of segmentation errors on instance-level clinical assessment.
AU - Baisen Chen
AU - Yukang Cheng
AU - Chunyan Ji
AU - Zhiming Cui
AU - Guanhua Xu
AU - Yuyu Sun
PY - 2026
DA - 2026/10/20
JO - Quantitative imaging in medicine and surgery
DO - 10.21037/qims-2026-1485
UR - https://europepmc.org/article/MED/42997901
AB - Automated lumbar facet joint degeneration grading requires both accurate anatomical segmentation and clinically meaningful region-level classification. We evaluated whether voxel-level segmentation performance was aligned with downstream degeneration grading. This retrospective study included 145 lumbar computed tomography (CT) examinations and 725 level-specific bilateral facet joint regions of interest (ROIs), including 165 severe and 560 non-severe regions. Weishaupt grades 0-1 were categorized as non-severe and grades 2-3 as severe. Scheme A used binary nnU-Net segmentation followed by 11 quantitative region-level features, training-fold-specific standardization, and logistic regression; an oracle comparison using ground-truth masks was compared with analysis using predicted masks. Scheme B used direct three-class segmentation. Patient-level five-fold cross-validation was performed, with 116 training and 29 validation patients per fold. The results were reported as mean ± standard deviation (SD). Scheme A achieved a binary segmentation Dice coefficient of 0.770±0.008. In the oracle comparison, the region-level area under the receiver operating characteristic (ROC) curve (AUC), recall, F1-score, accuracy, and case-level accuracy were 0.671±0.100, 0.538±0.172, 0.399±0.102, 0.640±0.051, and 0.579±0.096, respectively. Using predicted masks, the corresponding values were 0.650±0.068, 0.603±0.250, 0.375±0.072, 0.563±0.123, and 0.545±0.071. Scheme B achieved a native nnU-Net mean validation Dice of 0.493±0.044 and region-level AUC, recall, F1-score, accuracy, and case-level accuracy of 0.730±0.024, 0.445±0.051, 0.510±0.045, 0.808±0.015, and 0.738±0.058, respectively. Voxel-level segmentation and clinically oriented grading performance were not directly aligned. Scheme A provided stable anatomical segmentation but more variable downstream grading, whereas scheme B achieved better overall region- and case-level classification performance but lower severe-degeneration recall. Both spatial and clinical decision metrics should therefore be considered when evaluating automated facet joint grading systems.
SN - 2223-4292
SN - 2223-4306
ER -