基于深度学习的 CT 血管造影涂片工具:CTA-DEFACE

IF 3.7 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING European Radiology Experimental Pub Date : 2024-10-09 DOI:10.1186/s41747-024-00510-9
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
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引用次数: 0

摘要

人工神经网络(ANN)工具在计算机断层扫描血管造影(CTA)数据分析中的应用日益广泛,这凸显了加强数据保护措施的必要性。我们的目标是为 CTA 数据建立一个自动去污管道。在这项回顾性研究中,我们利用来自多机构队列的 CTA 数据来注释面罩(n = 100)并训练一个 ANN 模型,随后在外部机构的数据集(n = 50)上进行测试,并与公开可用的去污算法进行比较。应用人脸检测(MTCNN)和验证(FaceNet)网络来测量原始和污损 CTA 图像之间的相似性。通过计算骰子相似系数(DSC)、人脸检测概率和人脸相似度量来评估模型性能。CTA-DEFACE 模型有效地分割了 CTA 数据中的软脸部组织,测试集上的 DSC 为 0.94 ± 0.02(平均值 ± 标准偏差)。我们的模型与公开的玷污算法进行了基准测试。在应用人脸检测和验证网络后,我们的模型大幅降低了人脸检测概率(p
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Deep learning-based defacing tool for CT angiography: CTA-DEFACE.

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.

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来源期刊
European Radiology Experimental
European Radiology Experimental Medicine-Radiology, Nuclear Medicine and Imaging
CiteScore
6.70
自引率
2.60%
发文量
56
审稿时长
18 weeks
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