Today, fitting bespoke hearing aids involves injecting silicone into patients’ ears to produce ear canal molds. These are subsequently 3D scanned to create digital ear canal impressions. However, before digital impressions can be used they require a substantial amount of effort in manual 3D editing. In this article, we present computational methods to pre-process ear canal impressions. The aim is to create automation tools to assist the hearing aid design, manufacturing and fitting processes as well as normalizing anatomical data to assist the study of the outer ear canal’s morphology. The methods include classifying the handedness of the impression into left and right ear types, orienting the geometries onto the same coordinate system sense, and removing extraneous artifacts introduced by the silicone mold. We investigate the use of convolutional neural networks for performing these semantic tasks and evaluate their accuracy using a dataset of 3000 ear canal impressions. The neural networks proved highly effective at performing these tasks with 95.8% adjusted accuracy in classification, 92.3% within 20° angular error in registration and 93.4% intersection over union in segmentation.

中文翻译：

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使用卷积神经网络对耳道印模进行分类、配准和分割

如今，安装定制助听器需要将硅胶注射到患者的耳朵中以制作耳道模具。随后对这些进行 3D 扫描以创建数字耳道印模。然而，在使用数字印模之前，需要进行大量的手动 3D 编辑工作。在本文中，我们提出了预处理耳道印模的计算方法。目的是创建自动化工具来协助助听器设计、制造和验配过程，以及标准化解剖数据以协助外耳道形态的研究。这些方法包括将印模的用手习惯分为左耳和右耳类型，将几何形状定向到相同的坐标系感觉上，以及去除硅胶模具引入的无关伪影。我们研究了使用卷积神经网络执行这些语义任务，并使用 3000 个耳道印模的数据集评估其准确性。事实证明，神经网络在执行这些任务方面非常有效，分类调整精度为 95.8%，配准角度误差在 20° 以内为 92.3%，分割中交集比并集的交集精度为 93.4%。