Klinder Tobias, Wolz Robin, Lorenz Cristian, Franz Astrid, Ostermann Jörn
Institut für Informationsverarbeitung, Leibniz University of Hannover, Germany.
Med Image Comput Comput Assist Interv. 2008;11(Pt 1):227-34. doi: 10.1007/978-3-540-85988-8_28.
Including prior shape in the form of anatomical models is a well-known approach for improving segmentation results in medical images. Currently, most approaches are focused on the modeling and segmentation of individual objects. In case of object constellations, a simultaneous segmentation of the ensemble that uses not only prior knowledge of individual shapes but also additional information about spatial relations between the objects is often beneficial. In this paper, we present a two-scale framework for the modeling and segmentation of the spine as an example for object constellations. The global spine shape is expressed as a consecution of local vertebra coordinate systems while individual vertebrae are modeled as triangulated surface meshes. Adaptation is performed by attracting the model to image features but restricting the attraction to a former learned shape. With the developed approach, we obtained a segmentation accuracy of 1.0 mm in average for ten thoracic CT images improving former results.
将解剖模型形式的先验形状纳入其中是一种提高医学图像分割结果的知名方法。目前，大多数方法都集中在单个对象的建模和分割上。对于对象星座的情况，对整体进行同时分割，不仅使用单个形状的先验知识，还使用关于对象之间空间关系的额外信息，通常是有益的。在本文中，我们提出了一个两尺度框架，用于将脊柱作为对象星座的示例进行建模和分割。全局脊柱形状表示为局部椎骨坐标系的连续序列，而单个椎骨则建模为三角化表面网格。通过将模型吸引到图像特征来进行自适应，但将吸引力限制在先前学习的形状上。使用所开发的方法，我们对十幅胸部CT图像平均获得了1.0毫米的分割精度，改进了先前的结果。
