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首页> 外文期刊>Journal of Imaging Science and Technology >Non-Rigid Registration Based Active Appearance Models for 3D Medical Image Segmentation
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Non-Rigid Registration Based Active Appearance Models for 3D Medical Image Segmentation

机译：基于非刚性配准的3D医学图像分割主动外观模型

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摘要

Active shape models and active appearance models are getting increasingly popular in medical image segmentation applications. However, they are not suitable for three-dimensional (3D) images in their original form. This is due to the underlying shape representation (a point distribution model, PDM), which becomes impractical in 3D. Recently, it was shown that nonlinear registration algorithms can assist in the automatic creation of a 3D PDM. Based on this idea, we built a 3D active appearance model of brain structures. The model extracts the mean texture and the image deformation variation information from the training set of images. A special benefit is the inclusion of an extended region of interest into the model, making it suitable for segmentation of structures with poorly defined edges. We evaluated the model by applying it to the task of automatic segmentation of the hippocampi from magnetic resonance brain images. We found high accuracy of the model, which is comparable to the accuracy of the underlying registration method. The main benefit of the model-based segmentation over the registration-based segmentation is time, which is reduced from many hours (for registering an atlas to the image) to only a few minutes (for fitting the model to the image).
机译：主动形状模型和主动外观模型在医学图像分割应用中越来越受欢迎。但是，它们不适合原始形式的三维（3D）图像。这是由于基本的形状表示（点分布模型，PDM），在3D中变得不切实际。最近，研究表明非线性配准算法可以帮助自动创建3D PDM。基于这个想法，我们建立了大脑结构的3D活动外观模型。该模型从训练的图像集中提取平均纹理和图像变形变化信息。一个特殊的好处是将感兴趣的扩展区域包括在模型中，使其适用于分割边缘定义不明确的结构。我们通过将其应用于磁共振脑图像自动分割海马的任务来评估该模型。我们发现该模型具有很高的准确性，可与基础注册方法的准确性相媲美。与基于注册的细分相比，基于模型的细分的主要优势是时间，时间从许多小时（用于将图集注册到图像）减少到只有几分钟（用于将模型拟合到图像）。

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