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通过ResNet卷积神经网络进行鼻腔美学评估的自动框架

Maryam Ashoori¹, Reza A Zoroofi², Mohammad Sadeghi³

¹Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran. Maryam.ashoori@ut.ac.ir.

Journal of imaging informatics in medicine

|February 12, 2024

概括

这项研究引入了用于评估鼻底对称性的自动框架 (AF),帮助进行鼻整形和重建. AF准确地预测了鼻底的美学,与外科规划的专家评估保持一致.

科学领域:

医疗成像医学成像
计算机视觉计算机视觉
整形外科整形外科整形外科

背景情况:

鼻底美学在重建和整形外科手术中提出了复杂的挑战.
准确评估鼻底对称性对于成功的鼻整形和重建结果至关重要.

研究的目的:

开发一种新的自动框架 (AF) 来评估鼻底的美学和对称性.
提供一个工具,以协助手术前规划,手术内决策和术后评估.

主要方法:

一种用于识别鼻底地标的混合模型.
一个结合深层卷积神经网络 (CNN) 和多层感知神经网络 (MLP) 的模型,用于对称性预测.
利用数据增强技术来增强对鼻底图像的模型训练.

主要成果:

自动框架 (AF) 显示了与耳鼻喉科医生评级密切相关的结果.
AF有效地预测鼻底对称性,并识别不对称的区域.
视觉化证实了框架在鼻腔美学语义预测方面的能力.

结论:

拟议的自动框架为客观的鼻底美学评估提供了有价值的工具.
在鼻整形和重建手术中,AF可以显著提高对称性评估的精度和一致性.
这种深度学习方法促进了更好的外科规划和鼻腔手术的结果.

关键词:

自动化框架自动化框架组合模型是一个组合模型.混合动力模型混合动力模型标志性的地标标志性的地标鼻子底部的鼻子底部对称性对称性对称性对称性对称性

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