Song Qi, Liu Yinxiao, Liu Yunlong, Saha Punam K, Sonka Milan, Wu Xiaodong
Department of Electrical & Computer Engineering, University of Iowa, Iowa City, IA 52242, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):172-80. doi: 10.1007/978-3-642-15711-0_22.
The segmentation of soft tissues in medical images is a challenging problem due to the weak boundary, large deformation and serious mutual influence. We present a novel method incorporating both the shape and appearance information in a 3-D graph-theoretic framework to overcome those difficulties for simultaneous segmentation of prostate and bladder. An arc-weighted graph is constructed corresponding to the initial mesh. Both the boundary and region information is incorporated into the graph with learned intensity distribution, which drives the mesh to the best fit of the image. A shape prior penalty is introduced by adding weighted-arcs in the graph, which maintains the original topology of the model and constraints the flexibility of the mesh. The surface-distance constraints are enforced to avoid the leakage between prostate and bladder. The target surfaces are found by solving a maximum flow problem in low-order polynomial time. Both qualitative and quantitative results on prostate and bladder segmentation were promising, proving the power of our algorithm.
由于软组织边界模糊、变形大且相互影响严重，医学图像中软组织的分割是一个具有挑战性的问题。我们提出了一种新颖的方法，该方法在三维图论框架中融合了形状和外观信息，以克服这些困难，实现前列腺和膀胱的同时分割。对应于初始网格构建一个弧加权图。边界和区域信息都与学习到的强度分布一起纳入图中，这驱使网格与图像达到最佳拟合。通过在图中添加加权弧引入形状先验惩罚，这保持了模型的原始拓扑结构并限制了网格的灵活性。实施表面距离约束以避免前列腺和膀胱之间的渗漏。通过在低阶多项式时间内解决最大流问题来找到目标表面。前列腺和膀胱分割的定性和定量结果都很有前景，证明了我们算法的有效性。
