Inter-Subject Shape Correspondence Computation From Medical Images Without Organ Segmentation
Inter-Subject Shape Correspondence Computation From Medical Images Without Organ Segmentation
复制标题
无需器官分割的医学图像的受试者间形状对应计算
DOI：
10.1109/access.2019.2940643
复制
发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Wang, Hongkai
中科院分区：
文献类型：
--
作者：
Chen, Zhaofeng;Qiu, Tianshuang;Wang, Hongkai
统计形状模型（SSM）已被建立为医学图像分割，配准和解剖建模的强大的解剖先验。为了构建能够准确模拟受试者间解剖变化的SSM，计算训练样本之间的准确形状对应关系至关重要。为了实现这一目标，国家的最先进的形状对应计算方法总是需要繁琐的分割的训练图像，而他们很少关注的对应精度的关键解剖标志，如骨关节，血管分叉等，因此，形状对应的计算是耗时的对应精度是不完善的。为了解决这些问题，本文提出了一种新的形状对应计算方法，该方法通过将器官形状模板配准到训练图像来消除图像分割的需要。该方法允许人类专家在训练图像中指定关键解剖标志，以定义关键标志的对应关系。一个强度和地标相结合的策略，实现利用图像强度和专家地标，以获得准确的形状对应。该方法被评估用于基于计算机断层扫描（CT）图像的头部解剖结构SSM和脊柱SSM的构建。使用所提出的方法构造的SSM表现出更好的形状对应精度比其他国家的最先进的对应方法。特别地，该方法获得了颅骨的像素级表面对应精度（1.38 mm）和脊柱的子像素级精度（0.92 mm）。使用我们的方法构造的SSM的一般性和特异性也优于其他比较对应方法构造的SSM的上级。通过这种方法，我们提出了一种新的方法，它需要较少的人为干预，并产生更高质量的SSM具有更好的形状建模精度。
Statistical shape models (SSMs) have been established as robust anatomical priors for medical image segmentation, registration and anatomy modelling. To construct an SSM which accurately models the inter-subject anatomical variations, it is crucial to compute accurate shape correspondence between the training samples. To achieve this goal, the state-of-the-art shape correspondence computation methods always require tedious segmentation of the training images, while they seldom pay enough attention to the correspondence accuracy of key anatomical landmarks like the bone joints, vessel bifurcations, etc. As a result, the computation of shape correspondence is time-consuming and the correspondence accuracy is imperfect. To solve these problems, this paper proposes a novel shape correspondence computation approach which eliminates the need for image segmentation by registering an organ shape template to the training images. This method allows the human expert to specify key anatomical landmarks in the training images to define the correspondence of the crucial landmarks. An intensity-and-landmark-combined strategy is implemented to utilized both the image intensity and expert landmarks to obtain accurate shape correspondence. This method is evaluated for the construction of head anatomy SSM and spine SSM based on computed tomography (CT) images. The SSMs constructed using the proposed method demonstrates better shape correspondence accuracy than other state-of-the-arts correspondence methods. In particular, this method obtains pixel-level surface correspondence accuracy (1.38 mm) for the skull and sub-pixel level accuracy (0.92 mm) for the spine. The generalisability and specificity of the SSMs constructed using our method are also superior to SSMs constructed using other compared correspondence methods. With this method, we propose a novel approach which takes less human intervention and produces higher quality SSM with better shape modelling accuracy.