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使用卷积神经网络对耳道印象进行分类,注册和细分
Stylianos Dritsas1, Kenneth Wei De Chua2, Zhi Hwee Goh1
1Singapore University of Technology and Design, 8 Somapah Road, 487372, Singapore.
Medical image analysis
|March 26, 2024
概括
本研究介绍了用于处理3D耳道印象的自动计算方法,简化了助听器的装配. 卷积神经网络显著提高了准备数字耳朵模型的准确性和效率.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 医疗成像医学成像
背景情况:
- 定制助听器的装配传统上需要手动3D编辑耳道模具.
- 目前用于创建数字耳纹的方法是劳动密集型和耗时的.
- 对于形态学研究,需要对耳道数据进行标准化.
研究的目的:
- 开发用于预处理3D耳道印记的自动计算方法.
- 创建有助于助听器设计,制造和安装的工具.
- 为了使外耳道形态学研究的解剖学数据正常化.
主要方法:
- 使用卷积神经网络对耳道印记手性 (左/右) 的分类.
- 将3D几何形状定向到一个一致的坐标系.
- 细分技术可以从耳模中去除工件.
- 评估使用3000个耳道印象的数据集.
主要成果:
- 卷积神经网络在分类中实现了95.8%的调整精度.
- 注册准确度在20°角误差 (92.3%) 之内.
- 细分业绩产生了93.4%的交叉与工会.
结论:
- 耳道印象的自动预处理是可行的和有效的.
- 计算方法,特别是深度学习,可以显著减少助听器工作流程中的手工劳动.
- 开发的技术为改进助听器定制和解剖研究提供了潜力.
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