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Simultaneous Segmentation of Prostatic Zones Using Active Appearance Models With Multiple Coupled Levelsets.
Computer Vision and Image Understanding ( IF 4.5 ) Pub Date : 2013-09-01 , DOI: 10.1016/j.cviu.2012.11.013
Robert Toth 1 , Justin Ribault , John Gentile , Dan Sperling , Anant Madabhushi
Affiliation  

In this work we present an improvement to the popular Active Appearance Model (AAM) algorithm, that we call the Multiple-Levelset AAM (MLA). The MLA can simultaneously segment multiple objects, and makes use of multiple levelsets, rather than anatomical landmarks, to define the shapes. AAMs traditionally define the shape of each object using a set of anatomical landmarks. However, landmarks can be difficult to identify, and AAMs traditionally only allow for segmentation of a single object of interest. The MLA, which is a landmark independent AAM, allows for levelsets of multiple objects to be determined and allows for them to be coupled with image intensities. This gives the MLA the flexibility to simulataneously segmentation multiple objects of interest in a new image. In this work we apply the MLA to segment the prostate capsule, the prostate peripheral zone (PZ), and the prostate central gland (CG), from a set of 40 endorectal, T2-weighted MRI images. The MLA system we employ in this work leverages a hierarchical segmentation framework, so constructed as to exploit domain specific attributes, by utilizing a given prostate segmentation to help drive the segmentations of the CG and PZ, which are embedded within the prostate. Our coupled MLA scheme yielded mean Dice accuracy values of .81, .79 and .68 for the prostate, CG, and PZ, respectively using a leave-one-out cross validation scheme over 40 patient studies. When only considering the midgland of the prostate, the mean DSC values were .89, .84, and .76 for the prostate, CG, and PZ respectively.

中文翻译:

使用具有多个耦合水平集的主动外观模型同时分割前列腺区域。

在这项工作中,我们提出了对流行的主动外观模型 (AAM) 算法的改进,我们将其称为多级别集 AAM (MLA)。MLA 可以同时分割多个对象,并使用多个水平集(而不是解剖标志)来定义形状。AAM 传统上使用一组解剖标志来定义每个对象的形状。然而,地标可能很难识别,而且 AAM 传统上只允许分割单个感兴趣的对象。MLA 是一种具有里程碑意义的独立 AAM,允许确定多个对象的水平集,并允许它们与图像强度相结合。这使得 MLA 能够灵活地同时分割新图像中的多个感兴趣的对象。在这项工作中,我们应用 MLA 从一组 40 个直肠内 T2 加权 MRI 图像中分割前列腺包膜、前列腺周围区 (PZ) 和前列腺中央腺 (CG)。我们在这项工作中采用的 MLA 系统利用分层分割框架,其构建是为了利用特定领域的属性,通过利用给定的前列腺分割来帮助驱动嵌入前列腺内的 CG 和 PZ 的分割。我们的耦合 MLA 方案对前列腺、CG 和 PZ 的平均 Dice 准确度值分别为 0.81、0.79 和 0.68,使用留一法交叉验证方案对 40 多个患者进行研究。当仅考虑前列腺中腺时,前列腺、CG 和 PZ 的平均 DSC 值分别为 0.89、0.84 和 0.76。
更新日期:2019-11-01
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