Department of Neuroradiology, Heidelberg University Hospital, Heidelberg, Germany.
Division for Computational Neuroimaging, Heidelberg University Hospital, Heidelberg, Germany.
Eur Radiol Exp. 2024 Oct 9;8(1):111. doi: 10.1186/s41747-024-00510-9.
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 < 0.001) and similarity to the original CTA image (p < 0.001). The CTA-DEFACE model enabled robust and precise defacing of CTA data. The trained network is publicly accessible at www.github.com/neuroAI-HD/CTA-DEFACE . RELEVANCE STATEMENT: The ANN model CTA-DEFACE, developed for automatic defacing of CT angiography images, achieves significantly lower face detection probabilities and greater dissimilarity from the original images compared to a publicly available model. The algorithm has been externally validated and is publicly accessible. KEY POINTS: The developed ANN model (CTA-DEFACE) automatically generates facemasks for CT angiography images. CTA-DEFACE offers superior deidentification capabilities compared to a publicly available model. By means of graphics processing unit optimization, our model ensures rapid processing of medical images. Our model underwent external validation, underscoring its reliability for real-world application.
人工神经网络（ANN）工具在计算机断层血管造影（CTA）数据分析中的应用日益广泛，这凸显了提高数据保护措施的必要性。我们旨在建立一个用于 CTA 数据的自动掩蔽流水线。在这项回顾性研究中，我们使用了来自多机构队列的 CTA 数据来注释面罩（n=100）并训练 ANN 模型，然后在外部机构的数据集（n=50）上进行测试，并与公开可用的掩蔽算法进行比较。应用人脸检测（MTCNN）和验证（FaceNet）网络来衡量原始和掩蔽后的 CTA 图像之间的相似性。计算 Dice 相似系数（DSC）、面部检测概率和面部相似性度量来评估模型性能。CTA-DEFACE 模型有效地分割了 CTA 数据中的软组织，在测试集上的 DSC 为 0.94±0.02（平均值±标准差）。我们的模型与公开可用的掩蔽算法进行了基准比较。在应用人脸检测和验证网络后，我们的模型显示出显著降低的人脸检测概率（p<0.001）和与原始 CTA 图像的相似性（p<0.001）。CTA-DEFACE 模型能够稳健而精确地掩蔽 CTA 数据。训练有素的网络可在 www.github.com/neuroAI-HD/CTA-DEFACE 上公开获取。相关性声明：开发的 ANN 模型 CTA-DEFACE 用于自动掩蔽 CT 血管造影图像，与公开可用的模型相比，实现了显著更低的人脸检测概率和更大的与原始图像的不相似性。该算法已通过外部验证并可公开获取。要点：开发的 ANN 模型（CTA-DEFACE）自动生成 CT 血管造影图像的面罩。CTA-DEFACE 提供了比公开可用的模型更优越的去识别能力。通过图形处理单元优化，我们的模型确保了医疗图像的快速处理。我们的模型经过了外部验证，强调了其在实际应用中的可靠性。
