Bibliographic Details
| Title: |
Deep learning-based defacing tool for CT angiography: CTA-DEFACE |
| Authors: |
Mustafa Ahmed Mahmutoglu, Aditya Rastogi, Marianne Schell, Martha Foltyn-Dumitru, Michael Baumgartner, Klaus Hermann Maier-Hein, Katerina Deike-Hofmann, Alexander Radbruch, Martin Bendszus, Gianluca Brugnara, Philipp Vollmuth |
| Source: |
European Radiology Experimental, Vol 8, Iss 1, Pp 1-7 (2024) |
| Publisher Information: |
SpringerOpen, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Medical physics. Medical radiology. Nuclear medicine |
| Subject Terms: |
Artificial intelligence, Computed tomography angiography, Data anonymization, Image processing (computer-assisted), Neural network (computer), Medical physics. Medical radiology. Nuclear medicine, R895-920 |
| More Details: |
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-DEFACE model effectively segmented soft face tissue in CTA data achieving a DSC of 0.94 ± 0.02 (mean ± standard deviation) on the test set. Our model was benchmarked against a publicly available defacing algorithm. After applying face detection and verification networks, our model showed substantially reduced face detection probability (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2509-9280 |
| Relation: |
https://doaj.org/toc/2509-9280 |
| DOI: |
10.1186/s41747-024-00510-9 |
| Access URL: |
https://doaj.org/article/0eb7272d53b34633a6b9fc8219c448c2 |
| Accession Number: |
edsdoj.0eb7272d53b34633a6b9fc8219c448c2 |
| Database: |
Directory of Open Access Journals |
