SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans.

IF 8.1 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Radiology-Artificial Intelligence Pub Date : 2024-11-06 DOI:10.1148/ryai.240005
Enamundram Naga Karthik, Jan Valošek, Andrew C Smith, Dario Pfyffer, Simon Schading-Sassenhausen, Lynn Farner, Kenneth A Weber, Patrick Freund, Julien Cohen-Adad
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Abstract

"Just Accepted" papers have undergone full peer review and have been accepted for publication in Radiology: Artificial Intelligence. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content. Purpose To develop a deep learning tool for the automatic segmentation of the spinal cord and intramedullary lesions in spinal cord injury (SCI) on T2-weighted MRI scans. Materials and Methods This retrospective study included MRI data acquired between July 2002 and February 2023 from 191 patients with SCI (mean age, 48.1 years ± 17.9 [SD]; 142 males). The data consisted of T2-weighted MRI acquired using different scanner manufacturers with various image resolutions (isotropic and anisotropic) and orientations (axial and sagittal). Patients had different lesion etiologies (traumatic, ischemic, and hemorrhagic) and lesion locations across the cervical, thoracic and lumbar spine. A deep learning model, SCIseg, was trained in a three-phase process involving active learning for the automatic segmentation of intramedullary SCI lesions and the spinal cord. The segmentations from the proposed model were visually and quantitatively compared with those from three other open-source methods (PropSeg, DeepSeg and contrast-agnostic, all part of the Spinal Cord Toolbox). Wilcoxon signed-rank test was used to compare quantitative MRI biomarkers of SCI (lesion volume, lesion length, and maximal axial damage ratio) derived from the manual reference standard lesion masks and biomarkers obtained automatically with SCIseg segmentations. Results SCIseg achieved a Dice score of 0.92 ± 0.07 (mean ± SD) and 0.61 ± 0.27 for spinal cord and SCI lesion segmentation, respectively. There was no evidence of a difference between lesion length (P = .42) and maximal axial damage ratio (P = .16) computed from manually annotated lesions and the lesion segmentations obtained using SCIseg. Conclusion SCIseg accurately segmented intramedullary lesions on a diverse dataset of T2-weighted MRI scans and extracted relevant lesion biomarkers (namely, lesion volume, lesion length, and maximal axial damage ratio). SCIseg is open-source and accessible through the Spinal Cord Toolbox (v6.2 and above). Published under a CC BY 4.0 license.

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SCIseg:在 T2 加权磁共振成像扫描中自动分割脊髓损伤的髓内病变。
"刚刚接受 "的论文经过同行评审,已被接受在《放射学》上发表:人工智能》上发表。这篇文章在以最终版本发表之前,还将经过校对、排版和校对审核。请注意,在制作最终校对稿的过程中,可能会发现一些可能影响内容的错误。目的 开发一种深度学习工具,用于在 T2 加权磁共振成像扫描中自动分割脊髓损伤(SCI)的脊髓和髓内病变。材料与方法 这项回顾性研究纳入了 2002 年 7 月至 2023 年 2 月期间从 191 名 SCI 患者(平均年龄为 48.1 岁 ± 17.9 [SD];142 名男性)处获取的 MRI 数据。数据包括使用不同扫描仪制造商、不同图像分辨率(各向同性和各向异性)和方向(轴向和矢状)采集的 T2 加权 MRI。患者的病因(外伤性、缺血性和出血性)和病变位置各不相同,遍及颈椎、胸椎和腰椎。深度学习模型 SCIseg 的训练分为三个阶段,其中包括主动学习,用于自动分割髓内 SCI 病变和脊髓。将所提模型的分割结果与其他三种开源方法(PropSeg、DeepSeg 和 contrast-agnostic,均为脊髓工具箱的一部分)的分割结果进行了直观和定量比较。使用Wilcoxon符号秩检验比较人工参考标准病变掩膜和SCIseg分割自动获得的SCI定量MRI生物标志物(病变体积、病变长度和最大轴向损伤比)。结果 SCIseg 对脊髓和 SCI 病灶分割的 Dice 评分分别为 0.92 ± 0.07(平均 ± SD)和 0.61 ± 0.27。根据人工标注的病灶计算出的病灶长度(P = .42)和最大轴向损伤率(P = .16)与使用 SCIseg 获得的病灶分割结果之间没有差异。结论 SCIseg 能在不同的 T2 加权磁共振成像扫描数据集上准确分割髓内病变,并提取相关的病变生物标志物(即病变体积、病变长度和最大轴向损伤比)。SCIseg 是开源的,可通过脊髓工具箱(v6.2 及以上版本)访问。以 CC BY 4.0 许可发布。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
16.20
自引率
1.00%
发文量
0
期刊介绍: Radiology: Artificial Intelligence is a bi-monthly publication that focuses on the emerging applications of machine learning and artificial intelligence in the field of imaging across various disciplines. This journal is available online and accepts multiple manuscript types, including Original Research, Technical Developments, Data Resources, Review articles, Editorials, Letters to the Editor and Replies, Special Reports, and AI in Brief.
期刊最新文献
Integrated Deep Learning Model for the Detection, Segmentation, and Morphologic Analysis of Intracranial Aneurysms Using CT Angiography. RSNA 2023 Abdominal Trauma AI Challenge Review and Outcomes Analysis. SCIseg: Automatic Segmentation of Intramedullary Lesions in Spinal Cord Injury on T2-weighted MRI Scans. Combining Biology-based and MRI Data-driven Modeling to Predict Response to Neoadjuvant Chemotherapy in Patients with Triple-Negative Breast Cancer. Optimizing Performance of Transformer-based Models for Fetal Brain MR Image Segmentation.
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