Dataset Information

A neural network approach to segment brain blood vessels in digital subtraction angiography.

ABSTRACT:

Background and objective

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.

Methods

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.

Results

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.

Conclusions

Our findings show that the deep learning approach can achieve satisfactory performance as a computer-aided analysis tool to assist clinicians in diagnosing CVDs.

SUBMITTER: Zhang M

PROVIDER: S-EPMC7518214 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

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Publications

A neural network approach to segment brain blood vessels in digital subtraction angiography.

Zhang Min M Zhang Chen C Wu Xian X Cao Xinhua X Young Geoffrey S GS Chen Huai H Xu Xiaoyin X

Computer methods and programs in biomedicine 20191102

<h4>Background and objective</h4>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 ...[more]

PMID: 31710990

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Project description:BackgroundCurrently, no clear diagnostic indicator of vertebral artery hypoplasia (VAH) or intracranial stenosis exists in clinic. This study aims to study the feasibility of neck-brain integrated ultrasound for evaluating stenosis of the intracranial segment in the vertebral artery by comparing with those of computed tomography angiography (CTA) and digital subtraction angiography (DSA).MethodsClinical data of 138 patients who presented to our hospital with dizziness and headache as the main symptoms between April 2018 and June 2019 were retrospectively analyzed. Patients were divided into observation group and unilateral VAH group. The diagnosing accuracy of ultrasound, CTA, and DAS, the vertebral artery diameter (VAD), blood flow, end diastolic velocity (EDV), peak systolic velocity (PSV) values, resistance indexes (RIs), and difference of RI were compared between the two groups. The sensitivity, specificity and accuracy of ultrasound hemodynamic indicators in assessing stenosis of the intracranial segment of the vertebral artery were calculated.ResultsThe accuracy rates of mild stenosis and non-stenosis with neck-brain integrated ultrasound were significantly different from those with CTA and DSA examination (P<0.05). The VAD, blood flow, EDV and PSV values of the affected side in the observation group were lower than those of the VAH side in the unilateral VAH group, while the RI and RI difference value were significantly higher than the unilateral VAH group (P<0.05). When the predicted boundary value was 2.084 mm (VAD), 56.41 mL/min (blood flow), 8.47 cm/s (EDV), 0.743 (RI), and 0.149 (difference of RI), the sensitivity and specificity were (86.43%, 49.45%), (88.67%, 47.42%), (80.47%, 68.71%), (82.71%, 84.66%), and (95.84%, 80.11%), respectively.ConclusionsNeck-brain integrated ultrasound is of great value as a noninvasive examination in the diagnosis of stenotic lesions of the internal cervical vertebral artery. VAD, EDV, blood flow, RI, and the difference in RI can be used as indicators to evaluate stenosis of the internal carotid segment of the vertebral artery and provide a reliable basis for the clinical diagnosis of posterior circulation ischemia.

Dataset Information

A neural network approach to segment brain blood vessels in digital subtraction angiography.

Background and objective

Methods

Results

Conclusions

Publications

A neural network approach to segment brain blood vessels in digital subtraction angiography.

Similar Datasets

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