Tsai A, Wells W, Tempany C, Grimson E, Willsky A
Laboratory for Information and Decision Systems (LIDS), Massachusetts Institute of Technology (MIT), Room #35-427, Cambridge, MA 02139, USA.
Med Image Anal. 2004 Dec;8(4):429-45. doi: 10.1016/j.media.2004.01.003.
This paper presents extensions which improve the performance of the shape-based deformable active contour model presented earlier in [IEEE Conf. Comput. Vision Pattern Recog. 1 (2001) 463] for medical image segmentation. In contrast to that previous work, the segmentation framework that we present in this paper allows multiple shapes to be segmented simultaneously in a seamless fashion. To achieve this, multiple signed distance functions are employed as the implicit representations of the multiple shape classes within the image. A parametric model for this new representation is derived by applying principal component analysis to the collection of these multiple signed distance functions. By deriving a parametric model in this manner, we obtain a coupling between the multiple shapes within the image and hence effectively capture the co-variations among the different shapes. The parameters of the multi-shape model are then calculated to minimize a single mutual information-based cost criterion for image segmentation. The use of a single cost criterion further enhances the coupling between the multiple shapes as the deformation of any given shape depends, at all times, upon every other shape, regardless of their proximity. We found that this resulting algorithm is able to effectively utilize the co-dependencies among the different shapes to aid in the segmentation process. It is able to capture a wide range of shape variability despite being a parametric shape-model. And finally, the algorithm is robust to large amounts of additive noise. We demonstrate the utility of this segmentation framework by applying it to a medical application: the segmentation of the prostate gland, the rectum, and the internal obturator muscles for MR-guided prostate brachytherapy.
本文介绍了一些扩展内容，这些扩展改进了[《IEEE计算机视觉与模式识别会议论文集》第1卷（2001年）第463页]中先前提出的基于形状的可变形活动轮廓模型在医学图像分割方面的性能。与先前的工作不同，我们在本文中提出的分割框架允许以无缝方式同时分割多个形状。为实现这一点，多个符号距离函数被用作图像中多个形状类别的隐式表示。通过对这些多个符号距离函数的集合应用主成分分析，推导出了这种新表示的参数模型。通过以这种方式推导参数模型，我们在图像中的多个形状之间获得了一种耦合，从而有效地捕捉了不同形状之间的协变关系。然后计算多形状模型的参数，以最小化用于图像分割的基于互信息的单一成本准则。使用单一成本准则进一步增强了多个形状之间的耦合，因为任何给定形状的变形在任何时候都取决于其他每个形状，而不管它们的距离远近。我们发现，由此产生的算法能够有效地利用不同形状之间的相互依赖关系来辅助分割过程。尽管它是一个参数形状模型，但它能够捕捉广泛的形状变化。最后，该算法对大量加性噪声具有鲁棒性。我们通过将其应用于一个医学应用来证明这个分割框架的实用性：用于磁共振引导前列腺近距离放射治疗的前列腺、直肠和闭孔内肌的分割。
