The herniation of cerebellum through the foramen magnum may block the normal flow of cerebrospinal fluid determining a severe disorder called Chiari I Malformation (CM-I). Different surgical options are available to help patients, but there is no standard to select the optimal treatment. This paper proposes a fully automated method to select the optimal intervention. It is based on morphological parameters of the brain, posterior fossa and cerebellum, estimated by processing sagittal magnetic resonance images (MRI). The processing algorithm is based on a non-rigid registration by a balanced multi-image generalization of demons method. Moreover, a post-processing based on active contour was used to improve the estimation of cerebellar hernia. This method allowed to delineate the boundaries of the regions of interest with a percentage of agreement with the delineation of an expert of about 85%. Different features characterizing the estimated regions were then extracted and used to develop a classifier to identify the optimal surgical treatment. Classification accuracy on a database of 50 patients was about 92%, with a predictive value of 88% (tested with a leave-one-out approach).

中文翻译：

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脑结构的自动形态学测量和 Chiari I 畸形最佳手术干预的识别。

小脑通过枕骨大孔突出可能会阻碍脑脊液的正常流动，从而导致称为 Chiari I 畸形 (CM-I) 的严重疾病。有不同的手术选择可以帮助患者，但没有标准来选择最佳治疗方案。本文提出了一种全自动方法来选择最佳干预措施。它基于大脑、后颅窝和小脑的形态参数，通过处理矢状磁共振图像 (MRI) 进行估计。该处理算法基于通过恶魔方法的平衡多图像泛化的非刚性配准。此外，基于活动轮廓的后处理用于改进小脑疝的估计。这种方法允许划定感兴趣区域的边界，与专家划定的一致性百分比约为 85%。然后提取表征估计区域的不同特征并用于开发分类器以识别最佳手术治疗。50 名患者的数据库的分类准确率约为 92%，预测值为 88%（使用留一法测试）。