Deep neural network-based detection and segmentation of intracranial aneurysms on 3D rotational DSA.

ABSTRACT:

Objective

Accurate diagnosis and measurement of intracranial aneurysms are challenging. This study aimed to develop a 3D convolutional neural network (CNN) model to detect and segment intracranial aneurysms (IA) on 3D rotational DSA (3D-RA) images.

Methods

3D-RA images were collected and annotated by 5 neuroradiologists. The annotated images were then divided into three datasets: training, validation, and test. A 3D Dense-UNet-like CNN (3D-Dense-UNet) segmentation algorithm was constructed and trained using the training dataset. Diagnostic performance to detect aneurysms and segmentation accuracy was assessed for the final model on the test dataset using the free-response receiver operating characteristic (FROC). Finally, the CNN-inferred maximum diameter was compared agains

SUBMITTER: Liu X 

PROVIDER: S-EPMC8493355 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature