Validation of experts versus atlas-based and automatic registration methods for subthalamic nucleus targeting on MRI
Validation of experts versus atlas-based and automatic registration methods for subthalamic nucleus targeting on MRI
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DOI：
10.1007/s11548-006-0007-y
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发表时间：
2006-03-01
影响因子：
3
通讯作者：
Thiran, Jean-Philippe
中科院分区：
文献类型：
--
作者：
Castro, F. Javier Sanchez;Pollo, Claudio;Thiran, Jean-Philippe
目的在功能性立体定向神经外科手术中，精确的靶点定位是手术成功和手术时间的基石之一。丘脑底核（Subthalamic nucleus，简称FDN）是帕金森病（Parkinson's disease，简称PD）脑深部电刺激治疗的常用靶点。不幸的是，在常见的医学成像模式中，通常不能清楚地看到肿瘤，这证明了使用基于图谱的分割技术来推断肿瘤位置的合理性。材料与方法本研究纳入了8例双侧植入PD患者。术前获取三维T1加权序列和反转恢复T2加权冠状位切片。我们提出了一种方法，用于建设的地面真理的位置和计划，允许两者，执行不同的非刚性配准算法之间的比较，并评估其可用性，以自动定位的位置。结果：在识别血管瘤位置方面，专家内变异性为1.06 ± 0.61mm，而最佳非刚性配准方法的误差为1.80 ± 0.62mm。另一方面，统计测试表明，仅具有12个自由度的仿射配准对于该应用是不够的。结论使用我们的验证评估计划，我们证明了自动的视网膜定位是可能的，准确的非刚性配准算法。
Objects In functional stereotactic neurosurgery, one of the cornerstones upon which the success and the operating time depends is an accurate targeting. The subthalamic nucleus (STN) is the usual target involved when applying deep brain stimulation for Parkinson's disease (PD). Unfortunately, STN is usually not clearly visible in common medical imaging modalities, which justifies the use of atlas-based segmentation techniques to infer the STN location. Materials and methods Eight bilaterally implanted PD patients were included in this study. A three-dimensional T1-weighted sequence and inversion recovery T2-weighted coronal slices were acquired pre-operatively. We propose a methodology for the construction of a ground truth of the STN location and a scheme that allows both, to perform a comparison between different non-rigid registration algorithms and to evaluate their usability to locate the STN automatically. Results The intra-expert variability in identifying the STN location is 1.06 +/- 0.61mm while the best non-rigid registration method gives an error of 1.80 +/- 0.62mm. On the other hand, statistical tests show that an affine registration with only 12 degrees of freedom is not enough for this application. Conclusions Using our validation-evaluation scheme, we demonstrate that automatic STN localization is possible and accurate with non-rigid registration algorithms.