An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network

IF 2.9 2区工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-01-29 DOI:10.1007/s10278-024-00973-7

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Abstract

Nasal base aesthetics is an interesting and challenging issue that attracts the attention of researchers in recent years. With that insight, in this study, we propose a novel automatic framework (AF) for evaluating the nasal base which can be useful to improve the symmetry in rhinoplasty and reconstruction. The introduced AF includes a hybrid model for nasal base landmarks recognition and a combined model for predicting nasal base symmetry. The proposed state-of-the-art nasal base landmark detection model is trained on the nasal base images for comprehensive qualitative and quantitative assessments. Then, the deep convolutional neural networks (CNN) and multi-layer perceptron neural network (MLP) models are integrated by concatenating their last hidden layer to evaluate the nasal base symmetry based on geometry features and tiled images of the nasal base. This study explores the concept of data augmentation by applying the methods motivated via commonly used image augmentation techniques. According to the experimental findings, the results of the AF are closely related to the otolaryngologists’ ratings and are useful for preoperative planning, intraoperative decision-making, and postoperative assessment. Furthermore, the visualization indicates that the proposed AF is capable of predicting the nasal base symmetry and capturing asymmetry areas to facilitate semantic predictions. The codes are accessible at https://github.com/AshooriMaryam/Nasal-Aesthetic-Assessment-Deep-learning.

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利用 ResNet 卷积神经网络进行鼻腔美学评估的自动框架

摘要鼻基底美学是一个有趣而具有挑战性的问题，近年来吸引了众多研究人员的关注。有鉴于此，我们在本研究中提出了一种新型的鼻基底自动评估框架（AF），可用于改善鼻整形和鼻重建中的对称性。引入的自动框架包括一个用于识别鼻基底地标的混合模型和一个用于预测鼻基底对称性的组合模型。所提出的最先进的鼻基底地标检测模型在鼻基底图像上进行训练，以进行全面的定性和定量评估。然后，将深度卷积神经网络（CNN）和多层感知器神经网络（MLP）模型的最后一个隐藏层合并起来，根据几何特征和鼻基底的平铺图像来评估鼻基底对称性。本研究通过应用常用图像增强技术所激发的方法，探索了数据增强的概念。实验结果表明，AF 的结果与耳鼻喉科医生的评分密切相关，对术前规划、术中决策和术后评估非常有用。此外，可视化结果表明，所提出的 AF 能够预测鼻基底对称性并捕捉不对称区域，从而促进语义预测。代码可在 https://github.com/AshooriMaryam/Nasal-Aesthetic-Assessment-Deep-learning 上查阅。

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来源期刊

Journal of Digital Imaging 医学-核医学

CiteScore

7.50

自引率

6.80%

发文量

192

审稿时长

6-12 weeks

期刊介绍： The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.