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Automatic segmentation of dura for quantitative analysis of lumbar stenosis: A deep learning study with 518 CT myelograms.
Fan, Guoxin; Li, Yufeng; Wang, Dongdong; Zhang, Jianjin; Du, Xiaokang; Liu, Huaqing; Liao, Xiang.
Afiliação
  • Fan G; Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China.
  • Li Y; Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, China.
  • Wang D; Department of Sports Medicine, Eighth Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.
  • Zhang J; Department of Orthopaedics, Putuo People's Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Du X; Department of Pain Medicine, Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen, China.
  • Liu H; Department of Orthopedics, The People's Hospital of Wenshang County, Wenshang, Shandong, China.
  • Liao X; Artificial Intelligence Innovation Center, Research Institute of Tsinghua PearlRiverDelta, Guangzhou, China.
J Appl Clin Med Phys ; 25(7): e14378, 2024 Jul.
Article em En | MEDLINE | ID: mdl-38729652
ABSTRACT

BACKGROUND:

The diagnosis of lumbar spinal stenosis (LSS) can be challenging because radicular pain is not often present in the culprit-level localization. Accurate segmentation and quantitative analysis of the lumbar dura on radiographic images are key to the accurate differential diagnosis of LSS. The aim of this study is to develop an automatic dura-contouring tool for radiographic quantification on computed tomography myelogram (CTM) for patients with LSS.

METHODS:

A total of 518 CTM cases with or without lumbar stenosis were included in this study. A deep learning (DL) segmentation algorithm 3-dimensional (3D) U-Net was deployed. A total of 210 labeled cases were used to develop the dura-contouring tool, with the ratio of the training, independent testing, and external validation datasets being 1503030. The Dice score (DCS) was the primary measure to evaluate the segmentation performance of the 3D U-Net, which was subsequently developed as the dura-contouring tool to segment another unlabeled 308 CTM cases with LSS. Automatic masks of 446 slices on the stenotic levels were then meticulously reviewed and revised by human experts, and the cross-sectional area (CSA) of the dura was compared.

RESULTS:

The mean DCS of the 3D U-Net were 0.905 ± 0.080, 0.933 ± 0.018, and 0.928 ± 0.034 in the five-fold cross-validation, the independent testing, and the external validation datasets, respectively. The segmentation performance of the dura-contouring tool was also comparable to that of the second observer (the human expert). With the dura-contouring tool, only 59.0% (263/446) of the automatic masks of the stenotic slices needed to be revised. In the revised cases, there were no significant differences in the dura CSA between automatic masks and corresponding revised masks (p = 0.652). Additionally, a strong correlation of dura CSA was found between the automatic masks and corresponding revised masks (r = 0.805).

CONCLUSIONS:

A dura-contouring tool was developed that could automatically segment the dural sac on CTM, and it demonstrated high accuracy and generalization ability. Additionally, the dura-contouring tool has the potential to be applied in patients with LSS because it facilitates the quantification of the dural CSA on stenotic slices.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estenose Espinal / Mielografia / Tomografia Computadorizada por Raios X / Dura-Máter / Aprendizado Profundo / Vértebras Lombares Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Appl Clin Med Phys Assunto da revista: BIOFISICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Estenose Espinal / Mielografia / Tomografia Computadorizada por Raios X / Dura-Máter / Aprendizado Profundo / Vértebras Lombares Limite: Adult / Aged / Female / Humans / Male / Middle aged Idioma: En Revista: J Appl Clin Med Phys Assunto da revista: BIOFISICA Ano de publicação: 2024 Tipo de documento: Article País de afiliação: China