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Information tracking approach to segmentation of ultrasound imagery of the prostate

机译：信息跟踪方法对前列腺超声图像的分割

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摘要

The volume of the prostate is known to be a pivotal quantity used by clinicians to assess the condition of the gland during prostate cancer screening. As an alternative to palpation, an increasing number of methods for estimation of the volume of the prostate are based on using imagery data. The necessity to process large volumes of such data creates a need for automatic segmentation tools which would allow the estimation to be carried out with maximum accuracy and efficiency. In particular, the use of transrectal ultrasound (TRUS) imaging in prostate cancer screening seems to be becoming a standard clinical practice because of the high benefit-to-cost ratio of this imaging modality. Unfortunately, the segmentation of TRUS images is still hampered by relatively low contrast and reduced SNR of the images, thereby requiring the segmentation algorithms to incorporate prior knowledge about the geometry of the gland. In this paper, a novel approach to the problem of segmenting the TRUS images is described. The proposed approach is based on the concept of distribution tracking, which provides a unified framework for modeling and fusing image-related and morphological features of the prostate. Moreover, the same framework allows the segmentation to be regularized by using a new type of weak shape priors, which minimally bias the estimation procedure, while rendering the procedure stable and robust. The value of the proposed methodology is demonstrated in a series of in silico and in vivo experiments.
机译：已知前列腺的体积是临床医生在前列腺癌筛查期间用来评估腺体状况的关键量。作为触诊的替代方法，越来越多的用于估计前列腺体积的方法都基于使用图像数据。处理大量此类数据的必要性产生了对自动分割工具的需求，这将允许以最大的准确性和效率来进行估计。特别地，由于这种成像方式的高性价比，经直肠超声（TRUS）成像在前列腺癌筛查中的应用似乎已成为一种标准的临床实践。不幸的是，相对较低的对比度和降低的图像SNR仍然阻碍了TRUS图像的分割，从而要求分割算法结合有关腺体几何形状的先验知识。在本文中，描述了一种解决TRUS图像分割问题的新颖方法。所提出的方法基于分布跟踪的概念，该概念提供了用于建模和融合图像相关和前列腺形态特征的统一框架。而且，相同的框架允许通过使用新型的弱形状先验来对分割进行正则化，从而将估计过程最小化，同时使过程稳定且健壮。一系列计算机和体内实验证明了所提出方法的价值。

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