Open Access. Powered by Scholars. Published by Universities.®
Articles 1 - 2 of 2
Full-Text Articles in Engineering
Can We Explain Machine Learning-Based Prediction For Rupture Status Assessments Of Intracranial Aneurysms?, Nan Mu, M. Rezaeitaleshmahalleh, Z. Lyu, M. Wang, J. Tang, C. M. Strother, J. J. Gemmete, A. S. Pandey, J. Jiang
Can We Explain Machine Learning-Based Prediction For Rupture Status Assessments Of Intracranial Aneurysms?, Nan Mu, M. Rezaeitaleshmahalleh, Z. Lyu, M. Wang, J. Tang, C. M. Strother, J. J. Gemmete, A. S. Pandey, J. Jiang
Michigan Tech Publications
Although applying machine learning (ML) algorithms to rupture status assessment of intracranial aneurysms (IA) has yielded promising results, the opaqueness of some ML methods has limited their clinical translation. We presented the first explainability comparison of six commonly used ML algorithms: multivariate logistic regression (LR), support vector machine (SVM), random forest (RF), extreme gradient boosting (XGBoost), multi-layer perceptron neural network (MLPNN), and Bayesian additive regression trees (BART). A total of 112 IAs with known rupture status were selected for this study. The ML-based classification used two anatomical features, nine hemodynamic parameters, and thirteen morphologic variables. We utilized permutation feature importance, …
An Attention Residual U-Net With Differential Preprocessing And Geometric Postprocessing: Learning How To Segment Vasculature Including Intracranial Aneurysms, Nan Mu, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Jinshan Tang, Jingfeng Jiang
An Attention Residual U-Net With Differential Preprocessing And Geometric Postprocessing: Learning How To Segment Vasculature Including Intracranial Aneurysms, Nan Mu, Zonghan Lyu, Mostafa Rezaeitaleshmahalleh, Jinshan Tang, Jingfeng Jiang
Michigan Tech Publications
Objective
Intracranial aneurysms (IA) are lethal, with high morbidity and mortality rates. Reliable, rapid, and accurate segmentation of IAs and their adjacent vasculature from medical imaging data is important to improve the clinical management of patients with IAs. However, due to the blurred boundaries and complex structure of IAs and overlapping with brain tissue or other cerebral arteries, image segmentation of IAs remains challenging. This study aimed to develop an attention residual U-Net (ARU-Net) architecture with differential preprocessing and geometric postprocessing for automatic segmentation of IAs and their adjacent arteries in conjunction with 3D rotational angiography (3DRA) images.
Methods
The …