FSTIF-UNet: a deep learning-based method towards automatic segmentation of intracranial aneurysms in un-reconstructed 3D-RA.Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs.  This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images.  FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89s/case.  The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794).  The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
FSTIF-UNet
FSTIF-UNet: a deep learning-based method towards automatic segmentation of intracranial aneurysms in un-reconstructed 3D-RA.Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs.  This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images.  FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89s/case.  The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794).  The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
FSTIF-UNet
FSTIF-UNet: a deep learning-based method towards automatic segmentation of intracranial aneurysms in un-reconstructed 3D-RA.Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs.  This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images.  FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89s/case.  The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794).  The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
FSTIF-UNet
FSTIF-UNet: a deep learning-based method towards automatic segmentation of intracranial aneurysms in un-reconstructed 3D-RA.Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs.  This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images.  FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89s/case.  The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794).  The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
FSTIF-UNet
FSTIF-UNet: a deep learning-based method towards automatic segmentation of intracranial aneurysms in un-reconstructed 3D-RA.Segmentation of intracranial aneurysms (IAs) is an important step for the diagnosis and treatment of IAs.  This study aims to develop a deep-learning-based framework (defined as FSTIF-UNet) towards IAs segmentation in un-reconstructed 3D Rotational Angiography (3D-RA) images.  FSTIF-UNet achieves DSC, IoU, Sens, Haus, and F1-Score of 0.9109, 0.8586, 0.9314, 1.358 and 0.8883, respectively, and time taken for network segmentation is 0.89s/case.  The results show significant improvement in IA segmentation performance with FSTIF-UNet compared with baseline networks (with DSC from 0.8486 - 0.8794).  The proposed FSTIF-UNet establishes a practical method to assist the radiologists in clinical diagnosis.
FSTIF-UNet