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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 …