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首页> 外文会议>SPIE Conference on Biomedical Applications in Molecular, Structural, and Functional Imaging >Automated Segmentation of Ventricles from Serial Brain MRI for theQuantification of Volumetric Changes Associated withCommunicating Hydrocephalus in Patients with Brain Tumor
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Automated Segmentation of Ventricles from Serial Brain MRI for theQuantification of Volumetric Changes Associated withCommunicating Hydrocephalus in Patients with Brain Tumor

机译：来自连续脑研磨的心室的自动分割，用于患有脑肿瘤患者中与Communicated脑积水相关的体积变化

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Accurate ventricle volume estimates could improve the understanding and diagnosis of postoperative communicating hydrocephalus. For this category of patients, associated changes in ventricle volume can be difficult to identify, particularly over short time intervals. We present an automated segmentation algorithm that evaluates ventricle size from serial brain MRI examination. The technique combines serial T1-weighted images to increase SNR and segments the means image to generate a ventricle template. After pre-processing, the segmentation is initiated by a fuzzy c-means clustering algorithm to find the seeds used in a combination of fast marching methods and geodesic active contours. Finally, the ventricle template is propagated onto the serial data via non-linear registration. Serial volume estimates were obtained in an automated robust and accurate manner from difficult data.
机译：准确的心室估计可以改善术后沟通脑积水的理解和诊断。对于此类患者，脑室体积的相关变化可能难以识别，特别是在短时间内。我们提出了一种自动分割算法，评估来自串行脑MRI检查的心室大小。该技术结合了串行T1加权图像来增加SNR和段的平均图像以产生心室模板。在预处理之后，通过模糊C-Means聚类算法启动分割，以找到用于快速行进方法和测地有源轮廓的组合使用的种子。最后，心室模板通过非线性注册传播到串行数据上。以自动稳健和准确的方式从困难数据获得串行体积估计。

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