Bullitt E, Aylward S, Smith K, Mukherji S, Jiroutek M, Muller K
Medical Image Display and Analysis Group, University of North Carolina, Chapel Hill, NC 27599, USA.
Med Image Anal. 2001 Jun;5(2):157-69. doi: 10.1016/s1361-8415(01)00037-8.
We describe and evaluate methods that create detailed vessel trees by linking vessels that have been segmented from magnetic resonance angiograms (MRA). The tree-definition process can automatically exclude erroneous vessel segmentations. The parent-child connectivity information provided by our vessel trees is important to both surgical planning and to guidance of endovascular procedures. We evaluated the branch connection accuracy of our 3D vessel trees by asking two neuroradiologists to evaluate 140 parent-child connections comprising seven vascular trees against 17 digital subtraction angiography (DSA) views. Each reviewer rated each connection as (1) Correct, (2) Incorrect, (3) Partially correct (a minor error without clinical significance), or (4) Indeterminate. Analysis was summarized for each evaluator by calculating 95% confidence intervals for both the proportion completely correct and the proportion clinically acceptable (completely or partially correct). In order to protect the overall Type I error rate, alpha-splitting was done using a top down strategy. We additionally evaluated segmentation completeness by examining each slice in 11 MRA datasets in order to determine unlabeled vessels identifiable in cross-section following segmentation. Results indicate that only one vascular parent-child connection was judged incorrect by both reviewers. MRA segmentations appeared complete within MRA resolution limits. We conclude that our methods permit creation of detailed vascular trees from segmented 3D image data. We review the literature and compare other approaches to our own. We provide examples of clinically useful visualizations enabled by our methodology and taken from a visualization program now in clinical use.
我们描述并评估了通过连接从磁共振血管造影（MRA）中分割出的血管来创建详细血管树的方法。树定义过程可以自动排除错误的血管分割。我们的血管树提供的父子连接信息对于手术规划和血管内手术的引导都很重要。我们通过让两位神经放射科医生根据17张数字减影血管造影（DSA）视图评估包含七棵血管树的140个父子连接，来评估我们3D血管树的分支连接准确性。每位审阅者将每个连接评为（1）正确，（2）错误，（3）部分正确（无临床意义的小错误），或（4）不确定。通过计算完全正确比例和临床可接受比例（完全或部分正确）的95%置信区间，对每位评估者的分析进行了总结。为了保护总体I型错误率，采用自上而下的策略进行α分割。我们还通过检查11个MRA数据集中的每个切片来评估分割完整性，以确定分割后在横截面中可识别的未标记血管。结果表明，两位审阅者都仅判断一个血管父子连接错误。在MRA分辨率限制内，MRA分割看起来是完整的。我们得出结论，我们的方法允许从分割的3D图像数据创建详细的血管树。我们回顾了文献并将其他方法与我们自己的方法进行了比较。我们提供了由我们的方法实现的、取自目前正在临床使用的可视化程序的临床有用可视化示例。
