Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA.
Department of Electrical and Computer Engineering, The University of Iowa, Iowa City, IA 52242, USA; Department of Radiation Oncology, The University of Iowa, Iowa City, IA 52242, USA.
Comput Med Imaging Graph. 2018 Nov;69:96-111. doi: 10.1016/j.compmedimag.2018.08.004. Epub 2018 Aug 30.
Shape priors have been widely utilized in medical image segmentation to improve segmentation accuracy and robustness. A major way to encode such a prior shape model is to use a mesh representation, which is prone to causing self-intersection or mesh folding. Those problems require complex and expensive algorithms to mitigate. In this paper, we propose a novel shape prior directly embedded in the voxel grid space, based on gradient vector flows of a pre-segmentation. The flexible and powerful prior shape representation is ready to be extended to simultaneously segmenting multiple interacting objects with minimum separation distance constraint. The segmentation problem of multiple interacting objects with shape priors is formulated as a Markov Random Field problem, which seeks to optimize the label assignment (objects or background) for each voxel while keeping the label consistency between the neighboring voxels. The optimization problem can be efficiently solved with a single minimum s-t cut in an appropriately constructed graph. The proposed algorithm has been validated on two multi-object segmentation applications: the brain tissue segmentation in MRI images and the bladder/prostate segmentation in CT images. Both sets of experiments showed superior or competitive performance of the proposed method to the compared state-of-the-art methods.
形状先验已广泛应用于医学图像分割，以提高分割的准确性和鲁棒性。编码这种先验形状模型的主要方法是使用网格表示，这容易导致自交或网格折叠。这些问题需要复杂和昂贵的算法来缓解。在本文中，我们提出了一种新的形状先验，它直接嵌入在体素网格空间中，基于预分割的梯度向量流。灵活强大的先验形状表示准备好扩展到同时分割具有最小分离距离约束的多个相互作用的对象。具有形状先验的多个相互作用对象的分割问题被表述为马尔可夫随机场问题，该问题旨在优化每个体素的标签分配（对象或背景），同时保持相邻体素之间的标签一致性。该优化问题可以通过在适当构建的图中进行单次最小 s-t 切割来有效地解决。所提出的算法已经在两个多目标分割应用中得到验证：MRI 图像中的脑组织分割和 CT 图像中的膀胱/前列腺分割。两组实验都表明，与比较的最先进方法相比，所提出的方法具有更好或更有竞争力的性能。
