Semiautomatic medical image segmentation using knowledge of anatomic shape
Semiautomatic medical image segmentation using knowledge of anatomic shape
复制标题
使用解剖形状知识的半自动医学图像分割
DOI：
10.1117/12.45204
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发表时间：
1991
影响因子：
4.8
通讯作者：
J. Brinkley
中科院分区：
文献类型：
--
作者：
J. Brinkley
一个程序称为SCANNER（版本0.6）进行2-D交互式医学图像分割使用解剖形状的知识。该知识在径向轮廓模型中实现，径向轮廓模型是一种灵活的通用模型，可以准确地变形以适应数据，但也可以对2-D轮廓形状类的预期形状和变化范围进行编码。该模型可以描述圆的单值扭曲轮廓，它是从相似形状的轮廓训练集学习的。学习模型中的变化允许它为低级边缘检测器提供搜索区域，从而减少假边缘的发生率。该系统的初步评价进行了111个2-D CT图像从12例接受放射治疗计划的癌症中看到的结构。结果表明，该模型是能够捕获的横截面的预期形状和变化范围的几个临床上重要的结构（肝，肾，眼睛，和一些肿瘤），基于知识的方法应减少分割时间比目前的手动方法的一个因素之间的两个和十个，和有用的模型减少的结构的可变性增加。
A program called SCANNER (version 0.6) is described for performing 2-D interactive medical image segmentation using knowledge of anatomic shape. The knowledge is implemented in a radial contour model, which is a flexible, generic model that can accurately deform to fit the data, but which also encodes the expected shape and range of variation for a 2-D contour shape class. The model, which can describe contours that are single-valued distortions of a circle, is learned from training sets of similarly-shaped contours. Variation in the learned model allows it to provide search regions for low level edge detectors, thereby reducing the incidence of false edges. Initial evaluation of this system was performed for structures seen in 111 2-D CT images from 12 patients undergoing radiation treatment planning for cancer. The results suggest that the model is able to capture the cross-sectional expected shape and range of variation for several clinically-important structures (the liver, kidney, eye, and some tumors), that the knowledge-based approach should reduce the segmentation time over current manual methods by a factor between two and ten, and that the usefulness of the model decreases as variability of the structure increases.