Pitiot Alain, Delingette Hervé, Thompson Paul M, Ayache Nicholas
EPIDAURE Laboratory, INRIA, Sophia Antipolis, France.
Neuroimage. 2004;23 Suppl 1:S85-96. doi: 10.1016/j.neuroimage.2004.07.040.
We describe an automated 3-D segmentation system for in vivo brain magnetic resonance images (MRI). Our segmentation method combines a variety of filtering, segmentation, and registration techniques and makes maximum use of the available a priori biomedical expertise, both in an implicit and an explicit form. We approach the issue of boundary finding as a process of fitting a group of deformable templates (simplex mesh surfaces) to the contours of the target structures. These templates evolve in parallel, supervised by a series of rules derived from analyzing the template's dynamics and from medical experience. The templates are also constrained by knowledge on the expected textural and shape properties of the target structures. We apply our system to segment four brain structures (corpus callosum, ventricles, hippocampus, and caudate nuclei) and discuss its robustness to imaging characteristics and acquisition noise.
我们描述了一种用于活体脑磁共振成像（MRI）的自动化三维分割系统。我们的分割方法结合了多种滤波、分割和配准技术，并以隐式和显式形式最大限度地利用了现有的先验生物医学专业知识。我们将边界查找问题视为一个将一组可变形模板（单纯形网格表面）拟合到目标结构轮廓的过程。这些模板在一系列从分析模板动态和医学经验得出的规则的监督下并行演化。模板还受到关于目标结构预期纹理和形状属性的知识的约束。我们将我们的系统应用于分割四个脑结构（胼胝体、脑室、海马体和尾状核），并讨论其对成像特征和采集噪声的鲁棒性。
