Erickson B J, Avula R T
Department of Diagnostic Radiology, Mayo Foundation, Rochester MN 55905, USA.
J Digit Imaging. 1998 May;11(2):74-82. doi: 10.1007/BF03168729.
In this article, we describe the development and validation of an automatic algorithm to segment brain from extracranial tissues, and to classify intracranial tissues as cerebrospinal fluid (CSF), gray matter (GM), white matter (WM) or pathology. T1 weighted spin echo, dual echo fast spin echo (T2 weighted and proton density (PD) weighted images) and fast Fluid Attenuated Inversion Recovery (FLAIR) magnetic resonance (MR) images were acquired in 100 normal patients and 9 multiple sclerosis (MS) patients. One of the normal studies had synthesized MS-like lesions superimposed. This allowed precise measurement of the accuracy of the classification. The 9 MS patients were imaged twice in one week. The algorithm was applied to these data sets to measure reproducibility. The accuracy was measured based on the synthetic lesion images, where the true voxel class was known. Ninety-six percent of normal intradural tissue voxels (GM, WM, and CSF) were labeled correctly, and 94% of pathological tissues were labeled correctly. A low coefficient of variation (COV) was found (mean, 4.1%) for measurement of brain tissues and pathology when comparing MRI scans on the 9 patients. A totally automatic segmentation algorithm has been described which accurately and reproducibly segments and classifies intradural tissues based on both synthetic and actual images.
在本文中，我们描述了一种自动算法的开发与验证，该算法可将脑与颅外组织进行分割，并将颅内组织分类为脑脊液（CSF）、灰质（GM）、白质（WM）或病变组织。对100名正常患者和9名多发性硬化症（MS）患者采集了T1加权自旋回波、双回波快速自旋回波（T2加权和质子密度（PD）加权图像）以及快速液体衰减反转恢复（FLAIR）磁共振（MR）图像。其中一项正常研究叠加了类似MS的合成病变。这使得能够精确测量分类的准确性。9名MS患者在一周内进行了两次成像。将该算法应用于这些数据集以测量可重复性。基于合成病变图像测量准确性，其中真实体素类别是已知的。96%的正常硬膜内组织体素（GM、WM和CSF）被正确标记，94%的病理组织被正确标记。在比较9名患者的MRI扫描时，发现脑组织和病变测量的变异系数（COV）较低（平均值为4.1%）。已经描述了一种完全自动的分割算法，该算法基于合成图像和实际图像准确且可重复地分割和分类硬膜内组织。
