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A Skull Stripping Method Using Deformable Surface and Tissue Classification

机译：使用可变形表面和组织分类的颅骨剥离方法

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Many neuroimaging applications require an initial step of skull stripping to extract the cerebrum, cerebellum, and brain stem. We approach this problem by combining deformable surface models and a fuzzy tissue classification technique. Our assumption is that contrast exists between brain tissue (gray matter and white matter) and cerebrospinal fluid, which separates the brain from the extra-cranial tissue. We first analyze the intensity of the entire image to find an approximate centroid of the brain and initialize an ellipsoidal surface around it. We then perform a fuzzy tissue classification with bias field correction within the surface. Tissue classification and bias field are extrapolated to the entire image. The surface iteratively deforms under a force field computed from the tissue classification and the surface smoothness. Because of the bias field correction and tissue classification, the proposed algorithm depends less on particular imaging contrast and is robust to inhomogeneous intensity often observed in magnetic resonance images. We tested the algorithm on all T1 weighted images in the OASIS database, which includes skull stripping results using Brain Extraction Tool; the Dice scores have an average of 0.948 with a standard deviation of 0.017, indicating a high degree of agreement. The algorithm takes on average 2 minutes to run on a typical PC and produces a brain mask and membership functions for gray matter, white matter, and cerebrospinal fluid. We also tested the algorithm on T2 images to demonstrate its generality, where the same algorithm without parameter adjustment gives satisfactory results.
机译：许多神经影像机应用需要初始剥离的初始步骤以提取大脑，小脑和脑干。通过结合可变形的表面模型和模糊组织分类技术，我们接近这个问题。我们的假设是脑组织（灰质和白质）和脑脊液之间存在对比，其将脑与颅内组织分离。我们首先分析整个图像的强度，找到大脑的近似质心，并在其周围初始化椭圆形表面。然后，我们在表面内执行模糊组织分类。组织分类和偏置场被推断为整个图像。表面在从组织分类和表面平滑度计算的力场下迭代地变形。由于偏置场校正和组织分类，所提出的算法较少缺点特定的成像对比度，并且在磁共振图像中经常观察到的不均匀强度是鲁棒的。我们在Oasis数据库中的所有T1加权图像上测试了算法，包括使用脑提取工具的颅骨剥离结果;骰子得分的平均值为0.948，标准差为0.017，表示高度的协议。该算法平均需要2分钟才能在典型的PC上运行，并为灰质，白质和脑脊髓液产生脑面罩和隶属函数。我们还在T2图像上测试了算法以展示其一般性，其中没有参数调整的相同算法给出了令人满意的结果。

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