Abstract：
OBJECTIVE: This study proposes a process for detecting slices with bone marrow edema (BME), a typical finding of axSpA, using MRI scans as the input. This process does not require manual input of ROIs and provides the results of the judgment of the presence or absence of BME on a slice and the location of edema as the rationale for the judgment.
METHODS: First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME.
RESULTS: The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively.
CONCLUSIONS: This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.
METHODS: First, the signal intensity of the MRI scans of the sacroiliac joint was normalized to reduce the variation in signal values between scans. Next, slices containing synovial joints were extracted using a slice selection network. Finally, the BME slice detection network determines the presence or absence of the BME in each slice and outputs the location of the BME.
RESULTS: The proposed method was applied to 86 MRI scans collected from 15 hospitals in Japan. The results showed that the average absolute error of the slice selection process was 1.49 slices for the misalignment between the upper and lower slices of the synovial joint range. The accuracy, sensitivity, and specificity of the BME slice detection network were 0.905, 0.532, and 0.974, respectively.
CONCLUSIONS: This paper proposes a process to detect the slice with BME and its location as the rationale of the judgment from an MRI scan and shows its effectiveness using 86 MRI scans. In the future, we plan to develop a process for detecting other findings such as bone erosion from MR scans, followed by the development of a diagnostic support system.
摘要：
目的：这项研究提出了一种检测骨髓水肿（BME）切片的方法，axSpA的典型发现,使用MRI扫描作为输入。此过程不需要手动输入ROI，并提供判断切片上是否存在BME的结果以及水肿的位置作为判断的依据。
方法：首先，将骶髂关节MRI扫描的信号强度标准化,以减少扫描之间信号值的变化.接下来,使用切片选择网络提取包含滑膜关节的切片。最后,BME切片检测网络确定每个切片中是否存在BME，并输出BME的位置。
结果：将提出的方法应用于从日本15家医院收集的86次MRI扫描。结果表明，对于滑膜关节范围的上下切片之间的错位，切片选择过程的平均绝对误差为1.49切片。准确性,灵敏度,BME切片检测网络的特异性分别为0.905、0.532和0.974。
结论：本文提出了一种使用BME检测切片及其位置的方法，作为从MRI扫描中判断的基本原理，并使用86次MRI扫描显示了其有效性。在未来,我们计划开发一种检测其他发现的方法，例如MR扫描中的骨侵蚀，其次是诊断支持系统的开发。
方法：首先，将骶髂关节MRI扫描的信号强度标准化,以减少扫描之间信号值的变化.接下来,使用切片选择网络提取包含滑膜关节的切片。最后,BME切片检测网络确定每个切片中是否存在BME，并输出BME的位置。
结果：将提出的方法应用于从日本15家医院收集的86次MRI扫描。结果表明，对于滑膜关节范围的上下切片之间的错位，切片选择过程的平均绝对误差为1.49切片。准确性,灵敏度,BME切片检测网络的特异性分别为0.905、0.532和0.974。
结论：本文提出了一种使用BME检测切片及其位置的方法，作为从MRI扫描中判断的基本原理，并使用86次MRI扫描显示了其有效性。在未来,我们计划开发一种检测其他发现的方法，例如MR扫描中的骨侵蚀，其次是诊断支持系统的开发。
