Deep learning-based defacing tool for CT angiography: CTA-DEFACE.

ABSTRACT: The growing use of artificial neural network (ANN) tools for computed tomography angiography (CTA) data analysis underscores the necessity for elevated data protection measures. We aimed to establish an automated defacing pipeline for CTA data. In this retrospective study, CTA data from multi-institutional cohorts were utilized to annotate facemasks (n = 100) and train an ANN model, subsequently tested on an external institution's dataset (n = 50) and compared to a publicly available defacing algorithm. Face detection (MTCNN) and verification (FaceNet) networks were applied to measure the similarity between the original and defaced CTA images. Dice similarity coefficient (DSC), face detection probability, and face similarity measures were calculated to evaluate model performance. The CTA-D

SUBMITTER: Mahmutoglu MA 

PROVIDER: S-EPMC11465008 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature