A framework for intracranial aneurysm detection and rupture analysis on DSA.

BACKGROUND

Intracranial aneurysm is a severe cerebrovascular disease that can result in subarachnoid hemorrhage (SAH), leading to high incidence and mortality rates. Computer-aided detection of aneurysms can assist doctors in enhancing diagnostic accuracy. The analysis of aneurysm imaging holds considerable predictive value for aneurysm rupture. This paper presents a method for the detection of aneurysms and analysis of ruptures using digital subtraction angiography (DSA).

METHODS

A total of 263 aneurysms were analyzed, with 125 being ruptured and 138 being unruptured. Firstly, a filter based on the eigenvalues of the Hessian matrix was proposed for aneurysm detection. The filter's detection parameters can be automatically obtained through Bayesian optimization. Aneurysms were detected based on their structure and the response of the filter. Secondly, considering the variations in blood flow and morphology among aneurysms in DSA, intensity, texture, and blood perfusion features were extracted from the ruptured aneurysms and unruptured aneurysms. Subsequently, a sparse representation (SR) method was utilized to classify unruptured and ruptured aneurysms.

RESULTS

The experimental results for aneurysm detection showed that the F1-score was 94.1%. In the classification of ruptured and unruptured aneurysms, the accuracy, sensitivity, specificity, and area under curve (AUC) were 96.1%, 94.4%, 97.5%, and 0.982, respectively.

CONCLUSION

This paper presents a scheme combining an aneurysm detection filter and machine learning, offering a reliable solution for the diagnosis and prediction of aneurysm rupture.