A digital brain atlas for surgical planning, model-driven segmentation, and teaching
A digital brain atlas for surgical planning, model-driven segmentation, and teaching
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DOI：
10.1109/2945.537306
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发表时间：
1996-09-01
影响因子：
5.2
通讯作者：
Jolesz, FA
中科院分区：
文献类型：
--
作者：
Kikinis, R;Shenton, ME;Jolesz, FA
我们开发了人脑的三维(3D)数字化图谱，以可视化空间复杂的结构。它是为磁共振(MR)成像数据集而设计的。到目前为止，我们已经将该图谱用于外科计划、模型驱动的分割和教学。我们结合使用自动分割和监督分割方法，根据神经解剖学知识定义感兴趣区域。我们还使用3D表面渲染技术创建了一个大脑图谱，使我们能够可视化复杂的3D大脑结构。我们进一步将这些信息链接到脚本文件，以保存空间信息和神经解剖学知识。在此，我们介绍了可视化图谱的应用--手术计划、模型驱动的分割和神经解剖学的教学。这种数字化的人脑有可能为外科手术的规划提供重要的参考信息。它也可以作为一个强大的教学工具，因为当用户能够在3D空间中查看和旋转结构时，可以更容易地想象神经解剖结构之间的空间关系。此外，大脑图谱的每个元素都与用户控制的指针显示的姓名标签相关联。该图集拥有作为模型驱动细分的模板的主要承诺。使用这项技术，可以在新的大脑图像上同时表征许多感兴趣的区域。
We developed a three-dimensional (3D) digitized atlas of the human brain to visualize spatially complex structures. It was designed for use with magnetic resonance (MR) imaging data sets. Thus far, we have used this atlas for surgical planning, model-driven segmentation, and teaching. We used a combination of automated and supervised segmentation methods to define regions of interest based on neuroanatomical knowledge. We also used 3D surface rendering techniques to create a brain atlas that would allow us to visualize complex 3D brain structures. We further linked this information to script files in order to preserve both spatial information and neuroanatomical knowledge. We present here the application of the atlas for visualization-ih surgical planning for model-driven segmentation and for the teaching of neuroanatomy. This digitized human brain has the potential to provide important reference information for the planning of surgical procedures. It can also serve as a powerful teaching tool, since spatial relationships among neuroanatomical structures can be more readily envisioned when the user is able to view and rotate the structures in 3D space. Moreover, each element of the brain atlas is associated with a name tag, displayed by a user-controlled pointer. The atlas holds a major promise as a template for model-driven segmentation. Using this technique, many regions of interest can be characterized simultaneously on new brain images.