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A neural network approach to segment brain blood vessels in digital subtraction angiography
Min Zhang1, Chen Zhang2, Xian Wu2
1Departments of Radiology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA.
Insights
This study developed a deep learning method for automated brain blood vessel segmentation in digital subtraction angiography (DSA) images. The approach shows promise for aiding clinicians in diagnosing cerebrovascular diseases (CVDs).
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Biomedical Engineering
Background:
- Cerebrovascular diseases (CVDs) present significant clinical challenges due to abnormalities in brain blood vessels.
- Digital subtraction angiography (DSA) is a key diagnostic tool, but its interpretation is complex and time-consuming.
- Automated analysis of DSA images is needed to improve diagnostic efficiency and accuracy.
Purpose of the Study:
- To develop a computerized approach for automated segmentation of brain blood vessels in DSA images.
- To evaluate the performance of a deep learning model for this segmentation task.
Main Methods:
- A U-net based deep learning model was employed for brain blood vessel segmentation.
- The model incorporated pre-processing techniques to enhance image analysis.
- Performance was validated against manually segmented ground truth data.
Main Results:
- The deep learning approach achieved high accuracy (0.978) and specificity (0.994).
- Sensitivity was 0.76, and the Dice coefficient averaged 0.8268.
- The model demonstrated robust performance with low standard deviations across metrics.
Conclusions:
- The developed deep learning approach shows satisfactory performance for automated brain blood vessel segmentation.
- This method can serve as a valuable computer-aided analysis tool for clinicians diagnosing CVDs.
- Further integration into clinical workflows could enhance diagnostic capabilities for cerebrovascular diseases.
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.
