An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network.
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 reconst...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 2; pp. 455 - 471 |
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| Autores principales: | , , |
| Formato: | computer program equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=177626020&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 177626020 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Apr2024 vid: 37 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 177626020 177626020 177626020 10.1007/s10278-024-00973-7 177626020 ppf: 455 ppct: 16 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Automatic Framework for Nasal Esthetic Assessment by ResNet Convolutional Neural Network. aug: au: Ashoori, Maryam Zoroofi, Reza A. Sadeghi, Mohammad affil: https://ror.org/05vf56z40 Control and Intelligent Processing Center of Excellence, School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran sug: subj: Neural Networks (Computer) Aesthetics Nose Anatomy and Histology Algorithms Rhinoplasty Body Regions Models, Anatomic ab: 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. pubtype: Academic Journal doctype: computer program equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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