Deep learning‐based structure segmentation and intramuscular fat annotation on lumbar magnetic resonance imaging

• Published in: JOR-spine 2024-09, Vol.7 (3), p.e70003-n/a
• Main Authors: Xu, Yefu, Zheng, Shijie, Tian, Qingyi, Kou, Zhuoyan, Li, Wenqing, Xie, Xinhui, Wu, Xiaotao
• Format: Article
• Language: English
• Subjects: Annotations; Automation; Back pain; Boxes; Datasets; Deep learning; fatty infiltration; lumbar disc herniation; Magnetic resonance imaging; paraspinal muscles; Patients; Performance evaluation; segmentation; Semantics; Surgeons; Vertebrae
• ISSN: 2572-1143; 2572-1143
• DOI: 10.1002/jsp2.70003

Background Lumbar disc herniation (LDH) is a prevalent cause of low back pain. LDH patients commonly experience paraspinal muscle atrophy and fatty infiltration (FI), which further exacerbates the symptoms of low back pain. Magnetic resonance imaging (MRI) is crucial for assessing paraspinal muscle condition. Our study aims to develop a dual‐model for automated muscle segmentation and FI annotation on MRI, assisting clinicians evaluate LDH conditions comprehensively. Methods The study retrospectively collected data diagnosed with LDH from December 2020 to May 2022. The dataset was split into a 7:3 ratio for training and testing, with an external test set prepared to validate model generalizability. The model's performance was evaluated using average precision (AP), recall and F1 score. The consistency was assessed using the Dice similarity coefficient (DSC) and Cohen's Kappa. The mean absolute percentage error (MAPE) was calculated to assess the error of the model measurements of relative cross‐sectional area (rCSA) and FI. Calculate the MAPE of FI measured by threshold algorithms to compare with the model. Results A total of 417 patients being evaluated, comprising 216 males and 201 females, with a mean age of 49 ± 15 years. In the internal test set, the muscle segmentation model achieved an overall DSC of 0.92 ± 0.10, recall of 92.60%, and AP of 0.98. The fat annotation model attained a recall of 91.30%, F1 Score of 0.82, and Cohen's Kappa of 0.76. However, there was a decrease on the external test set. For rCSA measurements, except for longissimus (10.89%), the MAPE of other muscles was less than 10%. When comparing the errors of FI for each paraspinal muscle, the MAPE of the model was lower than that of the threshold algorithm. Conclusion The models demonstrate outstanding performance, with lower error in FI measurement compared to thresholding algorithms. Our study developed a deep learning model for automated muscle segmentation and fat annotation on MRI to aid lumbar disc herniation assessment. The model demonstrated outstanding performance, with lower error in fatty infiltration measurement compared to thresholding algorithms.