Background

Brain herniation is one of the fatal outcomes of increased intracranial pressure (ICP). It is caused due to the presence of hematoma or tumor mass in the brain. Ideal midline (iML) divides the healthy brain into two (right and left) nearly equal hemispheres. In the presence of hematoma, the midline tends to shift from its original position to the contralateral side of the mass and thus develops a deformed midline (dML).

New method

In this study, a convolutional neural network (CNN) was used to predict the deformed left and right hemispheres. The proposed algorithm was validated with non-contrast computed tomography (NCCT) of (n = 45) subjects with two types of brain hemorrhages - epidural hemorrhage (EDH): (n = 5) and intra-parenchymal hemorrhage (IPH): (n = 40)).

Results

The method demonstrated excellent potential in automatically predicting MLS with the average errors of 1.29 mm by location, 66.4 mm² by 2D area, and 253.73 mm³ by 3D volume. Estimated MLS could be well correlated with other clinical markers including hematoma volume - R² = 0.86 (EDH); 0.48 (IPH) and a Radiologist-defined severity score (RSS) - R² = 0.62 (EDH); 0.57 (IPH). RSS was found to be even better correlated (R² = 0.98 (EDH); 0.70 (IPH)), hence better predictable by a joint correlation between hematoma volume, midline pixel- or voxel-shift, and minimum distance of (ideal or deformed) midline from the hematoma (boundary or centroid).

Conclusion

All these predictors were computed automatically, which highlighted the excellent clinical potential of the proposed automated method in midline shift (MLS) estimation and severity prediction in hematoma decision support systems.

中文翻译：

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使用深度学习对来自非对比CT图像的脑疝进行定量分析

背景

脑疝是颅内压升高（ICP）的致命结果之一。它是由于脑中存在血肿或肿瘤块引起的。理想中线（iML）将健康的大脑分为几乎相等的两个半球（左右两个半球）。在存在血肿的情况下，中线往往会从其原始位置转移到肿块的对侧，从而形成变形的中线（dML）。

新方法

在这项研究中，使用卷积神经网络（CNN）预测变形的左右半球。拟议的算法已通过（n = 45）有两种类型的脑出血的受试者的非对比计算机断层扫描（NCCT）进行了验证-硬膜外出血（EDH）：（n = 5）和实质性内出血（IPH）：（n = 40））。

结果

该方法在自动预测MLS方面显示出极好的潜力，其平均误差按位置为1.29 mm，按2D面积为66.4 mm ²和按3D体积为253.73 mm ³。估计的MLS可能与其他临床指标（包括血肿体积-R ²  = 0.86（EDH））良好相关；0.48（IPH）和放射科医生定义的严重性评分（RSS）-R ²  = 0.62（EDH）；0.57（IPH）。发现RSS的相关性更好（R ²  = 0.98（EDH）; 0.70（IPH）），因此可以通过血肿量，中线像素或体素移动与（理想或变形的）最小距离之间的联合相关性更好地预测）血肿（边界或质心）的中线。

结论

所有这些预测因子都是自动计算的，这突显了所提出的自动化方法在血肿决策支持系统中线偏移（MLS）估计和严重性预测中的卓越临床潜力。