论文标题

DeepDefacer:通过U-NET图像分割自动删除面部特征

DeepDefacer: Automatic Removal of Facial Features via U-Net Image Segmentation

论文作者

Khazane, Anish, Hoachuck, Julien, Gorgolewski, Krzysztof J., Poldrack, Russell A.

论文摘要

磁共振成像(MRI)领域的最新进展已使临床医生和研究人员之间的大规模合作完成神经成像任务。但是,研究人员通常被迫使用过时的慢软件来匿名MRI图像进行发布。这些程序专门在3D图像上执行昂贵的数学操作,这些图像随着图像的体积的增加而迅速降低匿名速度。在本文中,我们介绍了DeepDefacer,这是DeepDefacer对MRI匿名化的应用,该应用程序使用简化的3D U-NET网络掩盖MRI图像中的面部区域,并在传统的De-nidentification软件上大幅提高了速度。我们对大脑开发组织(IXI)和国际脑图(ICBM)的MRI图像进行培训,并针对基线3D U-NET模型进行定量评估我们的模型,以进行骰子,回忆和精度分数。我们还针对Pydeface(一种传统的污损应用程序)评估了DeepDefacer,涉及一系列CPU和GPU设备的速度,并定性地评估了我们的模型的污损输出与Pydeface产生的地面真相图像。我们在本手稿结束时提供了指向PYPI计划的链接,以鼓励进一步研究深度学习到MRI匿名化的应用。

Recent advancements in the field of magnetic resonance imaging (MRI) have enabled large-scale collaboration among clinicians and researchers for neuroimaging tasks. However, researchers are often forced to use outdated and slow software to anonymize MRI images for publication. These programs specifically perform expensive mathematical operations over 3D images that rapidly slow down anonymization speed as an image's volume increases in size. In this paper, we introduce DeepDefacer, an application of deep learning to MRI anonymization that uses a streamlined 3D U-Net network to mask facial regions in MRI images with a significant increase in speed over traditional de-identification software. We train DeepDefacer on MRI images from the Brain Development Organization (IXI) and International Consortium for Brain Mapping (ICBM) and quantitatively evaluate our model against a baseline 3D U-Net model with regards to Dice, recall, and precision scores. We also evaluate DeepDefacer against Pydeface, a traditional defacing application, with regards to speed on a range of CPU and GPU devices and qualitatively evaluate our model's defaced output versus the ground truth images produced by Pydeface. We provide a link to a PyPi program at the end of this manuscript to encourage further research into the application of deep learning to MRI anonymization.

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