Freiman M, Kronman A, Esses S J, Joskowicz L, Sosna J
School of Eng. and Computer Science, The Hebrew Univ. of Jerusalem, Israel, USA.
Med Image Comput Comput Assist Interv. 2010;13(Pt 3):73-80. doi: 10.1007/978-3-642-15711-0_10.
We present a new non-parametric model constraint graph min-cut algorithm for automatic kidney segmentation in CT images. The segmentation is formulated as a maximum a-posteriori estimation of a model-driven Markov random field. A non-parametric hybrid shape and intensity model is treated as a latent variable in the energy functional. The latent model and labeling map that minimize the energy functional are then simultaneously computed with an expectation maximization approach. The main advantages of our method are that it does not assume a fixed parametric prior model, which is subjective to inter-patient variability and registration errors, and that it combines both the model and the image information into a unified graph min-cut based segmentation framework. We evaluated our method on 20 kidneys from 10 CT datasets with and without contrast agent for which ground-truth segmentations were generated by averaging three manual segmentations. Our method yields an average volumetric overlap error of 10.95%, and average symmetric surface distance of 0.79 mm. These results indicate that our method is accurate and robust for kidney segmentation.
我们提出了一种新的非参数模型约束图割算法，用于CT图像中的肾脏自动分割。分割被公式化为模型驱动的马尔可夫随机场的最大后验估计。一个非参数混合形状和强度模型被视为能量泛函中的一个潜在变量。然后，使用期望最大化方法同时计算使能量泛函最小化的潜在模型和标记图。我们方法的主要优点是它不假设固定的参数先验模型，该模型容易受到患者间变异性和配准误差的影响，并且它将模型和图像信息都整合到一个基于图割的统一分割框架中。我们在来自10个CT数据集的20个肾脏上评估了我们的方法，这些数据集有或没有造影剂，其真实分割是通过平均三次手动分割生成的。我们的方法产生的平均体积重叠误差为10.95%，平均对称表面距离为0.79毫米。这些结果表明，我们的方法在肾脏分割方面是准确且稳健的。
