ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images.
ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images.
复制标题
DOI：
10.1109/embc.2016.7591443
复制
发表时间：
2016-08
期刊：
影响因子：
--
通讯作者：
Gerig G
中科院分区：
文献类型：
--
作者：
Yushkevich PA;Yang Gao;Gerig G
从生物医学图像中获取定量测量通常需要分割，即查找并概述感兴趣的结构。多模态成像数据集（其中每个空间位置都有多种成像测量）越来越常见，特别是在 MRI 中。在全自动分割算法不可用或无法达到所需准确度水平的应用中，半自动分割可以成为手动分割的一种节省时间的替代方案，允许人类专家指导分割，同时最大限度地减少专家在可自动化的重复任务上花费的精力。然而，现有的 3D 图像分析工具很少支持多模态成像数据的半自动分割。本文描述了 ITK-SNAP 交互式图像可视化和分割工具的新扩展，该工具支持以同时利用所有可用模态信息的方式对多模态成像数据集进行半自动分割。该方法将随机森林分类器（由用户通过在图像中放置多个笔触进行训练）与主动轮廓分割算法相结合。新的多模态半自动分割方法在高级胶质母细胞瘤分割的背景下进行了评估。
Obtaining quantitative measures from biomedical images often requires segmentation, i.e., finding and outlining the structures of interest. Multi-modality imaging datasets, in which multiple imaging measures are available at each spatial location, are increasingly common, particularly in MRI. In applications where fully automatic segmentation algorithms are unavailable or fail to perform at desired levels of accuracy, semi-automatic segmentation can be a time-saving alternative to manual segmentation, allowing the human expert to guide segmentation, while minimizing the effort expended by the expert on repetitive tasks that can be automated. However, few existing 3D image analysis tools support semi-automatic segmentation of multi-modality imaging data. This paper describes new extensions to the ITK-SNAP interactive image visualization and segmentation tool that support semi-automatic segmentation of multi-modality imaging datasets in a way that utilizes information from all available modalities simultaneously. The approach combines Random Forest classifiers, trained by the user by placing several brushstrokes in the image, with the active contour segmentation algorithm. The new multi-modality semi-automatic segmentation approach is evaluated in the context of high-grade glioblastoma segmentation.