Fu Jachih, Chai Jyh-Wen, Chen Po-Lin, Ding Yu-Wen, Chen Hung-Chieh
Computer Aided Measurement and Diagnostic Systems Laboratory, Department of Industrial Engineering and Management, National Yunlin University of Science and Technology, Yunlin 640, Taiwan.
Department of Radiology, Taichung Veterans General Hospital, Taichung 407, Taiwan.
Biomedicines. 2022 Aug 22;10(8):2049. doi: 10.3390/biomedicines10082049.
Cerebrospinal fluid (CSF) hypovolemia is the core of spontaneous intracranial hypotension (SIH). More than 1000 magnetic resonance myelography (MRM) images are required to evaluate each subject. An effective spinal CSF quantification method is needed. In this study, we proposed a cascade artificial intelligence (AI) model to automatically segment spinal CSF. From January 2014 to December 2019, patients with SIH and 12 healthy volunteers (HVs) were recruited. We evaluated the performance of AI models which combined object detection (YOLO v3) and semantic segmentation (U-net or U-net++). The network of performance was evaluated using intersection over union (IoU). The best AI model was used to quantify spinal CSF in patients. We obtained 25,603 slices of MRM images from 13 patients and 12 HVs. We divided the images into training, validation, and test datasets with a ratio of 4:1:5. The IoU of Cascade YOLO v3 plus U-net++ (0.9374) was the highest. Applying YOLO v3 plus U-net++ to another 13 SIH patients showed a significant decrease in the volume of spinal CSF measured (59.32 ± 10.94 mL) at disease onset compared to during their recovery stage (70.61 ± 15.31 mL). The cascade AI model provided a satisfactory performance with regard to the fully automatic segmentation of spinal CSF from MRM images. The spinal CSF volume obtained through its measurements could reflect a patient's clinical status.
脑脊液（CSF）低血容量是自发性颅内低压（SIH）的核心。评估每个受试者需要1000多张磁共振脊髓造影（MRM）图像。因此需要一种有效的脊髓脑脊液定量方法。在本研究中，我们提出了一种级联人工智能（AI）模型来自动分割脊髓脑脊液。2014年1月至2019年12月，招募了SIH患者和12名健康志愿者（HV）。我们评估了结合目标检测（YOLO v3）和语义分割（U-net或U-net++）的AI模型的性能。使用交并比（IoU）评估性能网络。使用最佳AI模型对患者的脊髓脑脊液进行定量。我们从13名患者和12名HV中获得了25603张MRM图像切片。我们将图像按照4:1:5的比例分为训练、验证和测试数据集。级联YOLO v3加U-net++的IoU（0.9374）最高。将YOLO v3加U-net++应用于另外13名SIH患者，结果显示与恢复阶段相比，疾病发作时测量的脊髓脑脊液体积显著减少（59.32±10.94 mL），恢复阶段为（70.61±15.31 mL）。级联AI模型在从MRM图像中全自动分割脊髓脑脊液方面表现出令人满意的性能。通过测量获得的脊髓脑脊液体积可以反映患者的临床状态。
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