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Open Lumbar Spine Image Analysis: A 3D Slicer Extension for Segmentation, Grading, and Intervertebral Disc Height
Narasimharao Kowlagi1, Antti Kemppainen1,2, Terence McSweeney1
1Research Unit of Health Sciences and Technology, University of Oulu, Oulu, Finland.
Study Design:
Retrospective and cross-sectional study.
Objective:
The study aims to develop an open software for lumbar spine image analysis enabling no-code approach to lumbar spine segmentation, grading, and intervertebral Disc Height Index (DHI) calculations with robust evaluation of the application on 6 external data sets from diverse geographical regions.
Background:
The data sets used include NFBC1966 (Finland), HKDDC (Hong Kong), TwinsUK (UK), CETIR (Spain), NCSD (Hungary), SPIDER (Netherlands), and Mendeley (global). Thirty participants from each data set were sampled for external evaluation, and NFBC1966 was used for training. Annotation was performed on T2-weighted mid-sagittal slices of vertebral bodies L1 to S1 and intervertebral discs L1/2 to L5/S1.
Materials And Methods:
Open Lumbar Spine Image Analysis (OLSIA) application was developed to include no-code approach tools for automated segmentation, grading, DHI calculation, and batch processing capabilities by integrating the deep learning (DL) models. DL models were trained on the NFBC1966 data set with augmentation (histogram clipping, median filtering, and geometric scaling) to improve generalization. Interrater agreement was assessed using dice similarity coefficient (DSC), Bland-Altman (BA) analysis for DHI measurements and a paired t test for statistical significance.
Results:
Our application demonstrated 222-fold improvement in processing time compared with performing manually lumbar spine segmentation, grading and DHI calculation tasks. OLSIA's segmentation performance exhibited close correspondence with the interrater agreement across all 6 external data sets. Interrater reliability was high (mean DSC >90). Although paired t test on DHI measurements is significant ( P < 0.05), the mean difference (0.02) of DHI from the BA plots indicates low systematic bias.
Conclusion:
We introduced OLSIA, a user-friendly interface for lumbar spine segmentation, grading, and intervertebral DHI calculation. OLSIA empowers researchers from diverse backgrounds to efficiently use the no-code tools to accelerate their radiomics and lumbar spine image analysis workflows.
