Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.
Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests.
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DOI：
10.1109/tmi.2016.2519264
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发表时间：
2016-06
影响因子：
10.6
通讯作者：
Shen D
中科院分区：
文献类型：
--
作者：
Gao Y;Shao Y;Lian J;Wang AZ;Chen RC;Shen D
从CT图像中分割男性盆腔器官是前列腺癌放射治疗的先决条件。放射治疗的疗效在很大程度上取决于分割精度。然而，由于CT图像的低组织对比度以及盆腔器官的形状和外观的大变化，男性盆腔器官的准确分割是具有挑战性的。在现有的分割方法中，可变形模型是最流行的，因为形状先验可以很容易地结合到规则化的分割。然而，对初始化的敏感性通常限制了它们的性能，特别是对于具有大形状变化的分割器官。在本文中，我们提出了一种新的方法来指导变形模型，从而使他们对任意初始化的鲁棒性。具体来说，我们学习了一个位移回归器，它预测从任何图像体素的目标器官边界的基础上的局部补丁外观的3D位移。该回归器为变形模型的每个顶点提供一个非局部外力，克服了传统变形模型的初始化问题。为了学习一个可靠的位移回归，特别提出了两种策略。1)提出了一种多任务随机森林来与器官分类器联合学习位移回归器; 2）使用自动上下文模型在逐体素预测期间迭代地执行结构信息。对313例患者的313次计划CT扫描进行了广泛的实验，结果表明，我们的方法比其他基于分类或回归的方法以及其他几种现有的CT盆腔器官分割方法取得了更好的结果。
Segmenting male pelvic organs from CT images is a prerequisite for prostate cancer radiotherapy. The efficacy of radiation treatment highly depends on segmentation accuracy. However, accurate segmentation of male pelvic organs is challenging due to low tissue contrast of CT images, as well as large variations of shape and appearance of the pelvic organs. Among existing segmentation methods, deformable models are the most popular, as shape prior can be easily incorporated to regularize the segmentation. Nonetheless, the sensitivity to initialization often limits their performance, especially for segmenting organs with large shape variations. In this paper, we propose a novel approach to guide deformable models, thus making them robust against arbitrary initializations. Specifically, we learn a displacement regressor, which predicts 3D displacement from any image voxel to the target organ boundary based on the local patch appearance. This regressor provides a nonlocal external force for each vertex of deformable model, thus overcoming the initialization problem suffered by the traditional deformable models. To learn a reliable displacement regressor, two strategies are particularly proposed. 1) A multi-task random forest is proposed to learn the displacement regressor jointly with the organ classifier; 2) an auto-context model is used to iteratively enforce structural information during voxel-wise prediction. Extensive experiments on 313 planning CT scans of 313 patients show that our method achieves better results than alternative classification or regression based methods, and also several other existing methods in CT pelvic organ segmentation.