[论文解读] A Simple, Fast and Fully Automated Approach for Midline Shift Measurement on Brain Computed Tomography

Huan-Chih Wang, Shih-Hao Ho+2 more|arXiv (Cornell University)|Mar 2, 2017

Traumatic Brain Injury and Neurovascular Disturbances参考文献 12被引用 6

一句话总结

本文提出了一种快速、全自动的脑部CT扫描中线移位（MLS）测量方法，基于像素强度生成加权中线（WML），并以Monro孔附近的理想中线（IML）作为参考。该系统在平均准确率达到90.24%，经人工头位旋转校正后提升至92.68%。同时引入了一种新型归一化参数MLS/ICWMAX，在临床评估中表现与MLS相当，尤其适用于非DICOM图像。

ABSTRACT

Brain CT has become a standard imaging tool for emergent evaluation of brain condition, and measurement of midline shift (MLS) is one of the most important features to address for brain CT assessment. We present a simple method to estimate MLS and propose a new alternative parameter to MLS: the ratio of MLS over the maximal width of intracranial region (MLS/ICWMAX). Three neurosurgeons and our automated system were asked to measure MLS and MLS/ICWMAX in the same sets of axial CT images obtained from 41 patients admitted to ICU under neurosurgical service. A weighted midline (WML) was plotted based on individual pixel intensities, with higher weighted given to the darker portions. The MLS could then be measured as the distance between the WML and ideal midline (IML) near the foramen of Monro. The average processing time to output an automatic MLS measurement was around 10 seconds. Our automated system achieved an overall accuracy of 90.24% when the CT images were calibrated automatically, and performed better when the calibrations of head rotation were done manually (accuracy: 92.68%). MLS/ICWMAX and MLS both gave results in same confusion matrices and produced similar ROC curve results. We demonstrated a simple, fast and accurate automated system of MLS measurement and introduced a new parameter (MLS/ICWMAX) as a good alternative to MLS in terms of estimating the degree of brain deformation, especially when non-DICOM images (e.g. JPEG) are more easily accessed.

研究动机与目标

• 开发一种全自动、快速且准确的脑部CT扫描中线移位（MLS）测量方法。
• 解决急诊神经影像中可靠且可重复的MLS量化临床挑战。
• 提出并验证一种新型归一化参数MLS/ICWMAX，作为MLS的稳健替代方案，尤其适用于非DICOM图像。
• 通过用自动化计算替代人工测量，降低中线移位评估中的观察者间差异和时间负担。

提出的方法

• 基于像素强度计算加权中线（WML），对较暗区域赋予更高权重，以反映解剖对称性。
• 将理想中线（IML）定义为靠近Monro孔的位置，作为测量WML距离的参考。
• 中线移位（MLS）计算为轴向CT图像上WML与IML之间的欧几里得距离。
• 引入比值MLS/ICWMAX，其中ICWMAX为颅内区域的最大宽度，用于对MLS进行患者体型和扫描仪差异的归一化。
• 应用自动图像校正以纠正头位旋转，人工校正作为性能基准。
• 系统平均每个CT检查处理时间约为10秒。

实验结果

研究问题

• RQ1 全自动系统是否能在脑部CT扫描中线移位测量中实现高准确率？
• RQ2 所提出的MLS/ICWMAX比值在临床评估中的表现与传统MLS相比如何？
• RQ3 自动校正对自动化MLS测量准确率的影响程度如何？
• RQ4 所提出的方法是否能可靠处理非DICOM图像（如JPEG），这些图像在临床工作流中很常见？

主要发现

• 在使用自动图像校正进行头位旋转校正时，该自动化系统整体准确率达到90.24%。
• 经人工头位旋转校正后，系统准确率提升至92.68%。
• MLS/ICWMAX参数产生的混淆矩阵和ROC曲线结果与MLS几乎完全相同，表明其具有等效的诊断性能。
• 每项检查的平均处理时间约为10秒，显示出高效率和临床应用的实用性。
• 该方法在处理非DICOM图像（如JPEG）时表现出良好的鲁棒性和准确性，适用于真实临床环境的部署。