Gao Yaozong, Wang Li, Shao Yeqin, Shen Dinggang
Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
Department of Computer Science, University of North Carolina at Chapel Hill, USA.
Mach Learn Med Imaging. 2014;8679:93-100. doi: 10.1007/978-3-319-10581-9_12.
Segmenting the prostate from CT images is a critical step in the radio-therapy planning for prostate cancer. The segmentation accuracy could largely affect the efficacy of radiation treatment. However, due to the touching boundaries with the bladder and the rectum, the prostate boundary is often ambiguous and hard to recognize, which leads to inconsistent manual delineations across different clinicians. In this paper, we propose a learning-based approach for boundary detection and deformable segmentation of the prostate. Our proposed method aims to learn a boundary distance transform, which maps an intensity image into a boundary distance map. To enforce the spatial consistency on the learned distance transform, we combine our approach with the auto-context model for iteratively refining the estimated distance map. After the refinement, the prostate boundaries can be readily detected by finding the valley in the distance map. In addition, the estimated distance map can also be used as a new external force for guiding the deformable segmentation. Specifically, to automatically segment the prostate, we integrate the estimated boundary distance map into a level set formulation. Experimental results on 73 CT planning images show that the proposed distance transform is more effective than the traditional classification-based method for driving the deformable segmentation. Also, our method can achieve more consistent segmentations than human raters, and more accurate results than the existing methods under comparison.
从CT图像中分割前列腺是前列腺癌放射治疗计划中的关键步骤。分割精度在很大程度上会影响放射治疗的效果。然而，由于前列腺与膀胱和直肠边界相互接触，其边界往往模糊不清，难以识别，这导致不同临床医生手动勾勒的结果不一致。在本文中，我们提出了一种基于学习的前列腺边界检测和可变形分割方法。我们提出的方法旨在学习一种边界距离变换，将强度图像映射为边界距离图。为了在学习到的距离变换上强制实现空间一致性，我们将我们的方法与自动上下文模型相结合，以迭代细化估计的距离图。细化之后，通过在距离图中寻找谷底可以很容易地检测到前列腺边界。此外，估计的距离图还可以用作引导可变形分割的新外力。具体来说，为了自动分割前列腺，我们将估计的边界距离图集成到水平集公式中。在73幅CT规划图像上的实验结果表明，所提出的距离变换在驱动可变形分割方面比传统的基于分类的方法更有效。而且，我们的方法能够实现比人工评分者更一致的分割，并且比现有对比方法得到更准确的结果。
