Yushkevich Paul A, Piven Joseph, Hazlett Heather Cody, Smith Rachel Gimpel, Ho Sean, Gee James C, Gerig Guido
Penn Image Computing and Science Laboratory, Department of Radiology, University of Pennsylvania, PA 19104-6274, USA.
Neuroimage. 2006 Jul 1;31(3):1116-28. doi: 10.1016/j.neuroimage.2006.01.015. Epub 2006 Mar 20.
Active contour segmentation and its robust implementation using level set methods are well-established theoretical approaches that have been studied thoroughly in the image analysis literature. Despite the existence of these powerful segmentation methods, the needs of clinical research continue to be fulfilled, to a large extent, using slice-by-slice manual tracing. To bridge the gap between methodological advances and clinical routine, we developed an open source application called ITK-SNAP, which is intended to make level set segmentation easily accessible to a wide range of users, including those with little or no mathematical expertise. This paper describes the methods and software engineering philosophy behind this new tool and provides the results of validation experiments performed in the context of an ongoing child autism neuroimaging study. The validation establishes SNAP intrarater and interrater reliability and overlap error statistics for the caudate nucleus and finds that SNAP is a highly reliable and efficient alternative to manual tracing. Analogous results for lateral ventricle segmentation are provided.
活动轮廓分割及其使用水平集方法的稳健实现是已确立的理论方法，在图像分析文献中已得到充分研究。尽管存在这些强大的分割方法，但临床研究的需求在很大程度上仍通过逐片手动追踪来满足。为了弥合方法学进展与临床常规之间的差距，我们开发了一个名为ITK-SNAP的开源应用程序，旨在使广泛的用户，包括那些几乎没有数学专业知识的用户，能够轻松地进行水平集分割。本文描述了这个新工具背后的方法和软件工程理念，并提供了在一项正在进行的儿童自闭症神经影像学研究背景下进行的验证实验结果。该验证确定了SNAP在同一评分者内部和不同评分者之间对于尾状核的可靠性以及重叠误差统计，并发现SNAP是手动追踪的一种高度可靠且高效的替代方法。还提供了侧脑室分割的类似结果。
