A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions.
A robust statistics driven volume-scalable active contour for segmenting anatomical structures in volumetric medical images with complex conditions.
复制标题
强大的统计驱动的体积可扩展主动轮廓，用于在复杂条件下分割体积医学图像中的解剖结构
DOI：
10.1186/s12938-016-0153-6
复制
发表时间：
2016-04-14
影响因子：
3.9
通讯作者：
Ma C
中科院分区：
文献类型：
--
作者：
Wang K;Ma C
背景医学图像中解剖结构的精确分割是计算机辅助介入系统发展的关键步骤。然而，复杂的图像条件，如强度不均匀性，噪声和弱目标边界，往往造成相当大的困难，医学图像分割。为了解决这些问题，提出了一种新的鲁棒统计驱动的体积可伸缩活动轮廓框架，用于从3D核磁共振（MR）和计算机断层扫描（CT）图像中提取目标边界。方法我们定义了一个基于初始种子标签的能量泛函和两个基于目标局部鲁棒统计特征的拟合函数。然后将该能量并入水平集方案中，该水平集方案驱动活动轮廓在对象边界的期望位置处演化和收敛。由于局部鲁棒统计和能量拟合项中的体积缩放函数，自适应地学习局部体积中的对象特征以指导轮廓的运动，从而保证了我们的方法应对强度不均匀性、噪声和弱边界的能力。此外，主动轮廓的初始化简化通过选择几个种子在对象和/或背景，以消除对initialization.ResultsThe所提出的方法的敏感性被应用到广泛的公共可用的体积医学图像具有挑战性的图像条件。通过与相应的地面真值进行比较，对白色物质（WM）、心房、尾状核和脑肿瘤等解剖结构的分割结果进行了定量评价。实验结果表明，该方法对脑白质、肝肿瘤、尾状核、脑肿瘤等的分割精度分别为0.9246 ± 0.0068、0.9043 ± 0.0131、0.8725 ± 0.0374、0.8802 ± 0.0595。由算法一和地面实况之间的重叠的Dice相似系数值测量。进一步的比较实验结果表明，所提出的方法在几个知名的分割方法的准确性和鲁棒性方面的理想性能。ConclusionWe提出了一种方法，准确分割体积医学图像与复杂的条件。基于大量的MR和CT数据，验证了分割的准确性、对噪声的鲁棒性和轮廓初始化。
BackgroundAccurate segmentation of anatomical structures in medical images is a critical step in the development of computer assisted intervention systems. However, complex image conditions, such as intensity inhomogeneity, noise and weak object boundary, often cause considerable difficulties in medical image segmentation. To cope with these difficulties, we propose a novel robust statistics driven volume-scalable active contour framework, to extract desired object boundary from magnetic resonance (MR) and computed tomography (CT) imagery in 3D.MethodsWe define an energy functional in terms of the initial seeded labels and two fitting functions that are derived from object local robust statistics features. This energy is then incorporated into a level set scheme which drives the active contour evolving and converging at the desired position of the object boundary. Due to the local robust statistics and the volume scaling function in the energy fitting term, the object features in local volumes are learned adaptively to guide the motion of the contours, which thereby guarantees the capability of our method to cope with intensity inhomogeneity, noise and weak boundary. In addition, the initialization of active contour is simplified by select several seeds in the object and/or background to eliminate the sensitivity to initialization.ResultsThe proposed method was applied to extensive public available volumetric medical images with challenging image conditions. The segmentation results of various anatomical structures, such as white matter (WM), atrium, caudate nucleus and brain tumor, were evaluated quantitatively by comparing with the corresponding ground truths. It was found that the proposed method achieves consistent and coherent segmentation accuracy of 0.9246 ± 0.0068 for WM, 0.9043 ± 0.0131 for liver tumors, 0.8725 ± 0.0374 for caudate nucleus, 0.8802 ± 0.0595 for brain tumors, etc., measured by Dice similarity coefficients value for the overlap between the algorithm one and the ground truth. Further comparative experimental results showed desirable performances of the proposed method over several well-known segmentation methods in terms of accuracy and robustness.ConclusionWe proposed an approach to accurate segment volumetric medical images with complex conditions. The accuracy of segmentation, robustness to noise and contour initialization were validated on the basis of extensive MR and CT volumes.