A neural network approach to segment brain blood vessels in digital subtraction angiography.
A neural network approach to segment brain blood vessels in digital subtraction angiography.
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DOI：
10.1016/j.cmpb.2019.105159
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发表时间：
2020-03
影响因子：
6.1
通讯作者：
Xu X
中科院分区：
文献类型：
--
作者：
Zhang M;Zhang C;Wu X;Cao X;Young GS;Chen H;Xu X
脑血管疾病（cvd）影响大量患者，往往具有毁灭性的后果。心血管疾病的特征是脑血管的异常，包括血管的突出、狭窄、扩张和分叉。cvd通常通过数字减影血管造影（DSA）诊断，但DSA的解释具有挑战性，因为必须仔细检查每条脑血管。这项工作的目的是开发一种计算机化的分析方法来自动分割脑血管。在这项工作中，我们提出了一种基于U-net的深度学习方法，结合预处理，来跟踪和分割DSA图像中的脑血管。我们使用准确性、灵敏度、特异性和Dice系数将深度学习方法给出的结果与人工标记的ground truth进行了比较。结果表明，该方法的准确率为0.978，标准差为0.00796，灵敏度为0.76，标准差为0.096，特异性为0.994，标准差为0.0036，平均Dice系数为0.8268，标准差为0.052。我们的研究结果表明，深度学习方法可以作为辅助临床医生诊断心血管疾病的计算机辅助分析工具取得令人满意的性能。
Cerebrovascular diseases (CVDs) affect a large number of patients and often have devastating outcomes. The hallmarks of CVDs are the abnormalities formed on brain blood vessels, including protrusions, narrows, widening, and bifurcation of the blood vessels. CVDs are often diagnosed by digital subtraction angiography (DSA) yet the interpretation of DSA is challenging as one must carefully examine each brain blood vessel. The objective of this work is to develop a computerized analysis approach for automated segmentation of brain blood vessels. In this work, we present a U-net based deep learning approach, combined with pre-processing, to track and segment brain blood vessels in DSA images. We compared the results given by the deep learning approach with manually marked ground truth using accuracy, sensitivity, specificity, and Dice coefficient. Our results showed that the proposed approach achieved an accuracy of 0.978, with a standard deviation of 0.00796, a sensitivity of 0.76 with a standard deviation of 0.096, a specificity of 0.994 with a standard deviation of 0.0036, and an average Dice coefficient was 0.8268 with a standard deviation of 0.052. Our findings show that the deep learning approach can achieve satisfactory performance as a computer-aided analysis tool to assist clinicians in diagnosing CVDs.
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影响因子：
5.1
作者：
COLLOREC, R;COATRIEUX, JL
通讯作者：
COATRIEUX, JL
DOI：
10.1016/j.compmedimag.2018.04.005
发表时间：
2018-09-01
影响因子：
5.7
作者：
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通讯作者：
Ko, Seok-Bum
影响因子：
3.5
作者：
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通讯作者：
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影响因子：
6.1
作者：
Klepaczko, Artur;Szczypinski, Piotr;Materka, Andrzej
通讯作者：
Materka, Andrzej
影响因子：
19.5
作者：
KASS, M;WITKIN, A;TERZOPOULOS, D
通讯作者：
TERZOPOULOS, D