一种从头部预测受试者特定头骨形状的深度学习方法,用于家庭面部康复的决策支持系统

IF 5.6 4区 医学 Q1 ENGINEERING, BIOMEDICAL
Irbm Pub Date : 2023-02-01 DOI:10.1016/j.irbm.2022.05.005
H.-Q. Nguyen , T.-N. Nguyen , V.-D. Tran , T.-T. Dao
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引用次数: 1

摘要

从头部预测人类头骨形状是开发计算机辅助视觉系统的一项复杂而富有挑战性的工程任务。在法医面部重建中,通常进行颅骨到面部的生成。通常使用经典的统计方法。然而,这种从头到头骨的关系仍然被误解。最近,新的深度学习(DL)模型在广泛的应用中显示出了其效率和鲁棒性。本研究旨在开发一种基于深度学习模型的新方法,从头部重建人类头骨形状。材料和方法开发并评估了从头到头骨的生成工作流程。为了训练和测试目的,建立了209名受试者的计算机断层扫描(CT)图像数据库。重建三维(3-D)头部和颅骨几何形状,然后提取它们各自的描述符(头部/颅骨体积、采样特征点和点到中心的距离、头部颅骨厚度、高斯曲率)。使用不同的学习配置实现并评估了两个深度学习模型(回归神经网络和长短期记忆(LSTM))。进行了10倍交叉验证。最后,对最佳和最差预测情况进行了分析和讨论。结果与长短期记忆模型相比,回归神经网络模型的10次交叉验证的平均误差具有较好的准确性。通过使用回归深度学习模型和最佳学习配置,DL预测的颅骨形状和基于CT的颅骨形状之间的平均误差范围为1.67mm至3.99mm。预测的颅骨形状与基于CT的颅骨形状之间的体积偏差小于5%。结论本研究表明,回归深度学习模型可以从给定的头部以良好的精度预测人类颅骨。这为从视觉传感器(如微软Kinect)到用于面部模拟康复的计算机辅助视觉系统快速生成人类头骨形状开辟了新的途径。作为展望,肌肉网络将被纳入目前的工作流程。然后,面部模拟动作将被跟踪并设置动画,以评估和优化康复动作和练习。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A Deep Learning Approach for Predicting Subject-Specific Human Skull Shape from Head Toward a Decision Support System for Home-Based Facial Rehabilitation

A Deep Learning Approach for Predicting Subject-Specific Human Skull Shape from Head Toward a Decision Support System for Home-Based Facial Rehabilitation

Objective

Prediction of human skull shape from head is a complex and challenging engineering task for the development of a computer-aided vision system. Skull-to-face generation has been commonly performed in forensic facial reconstruction. Classical statistical approaches were usually used. However, the head-to-skull relationship is still misunderstood. Recently, novel deep learning (DL) models have showed their efficiency and robustness for a large range of applications. The present study aimed to develop a novel approach based on deep learning models to reconstruct the human skull shape from head.

Material and methods

A head-to-skull generation workflow was developed and evaluated. A database of computed tomography (CT) images of 209 subjects was established for training and testing purposes. Three-dimension (3-D) head and skull geometries were reconstructed and then their respective descriptors (head/skull volumes, sampling feature points and point-to-center distances, head-skull thickness, Gaussian curvatures) were extracted. Two deep learning models (regression neural network and long-short term memory (LSTM)) were implemented and evaluated with different learning configurations. A 10-fold cross-validation was performed. Finally, the best and worst predicted cases were analyzed and discussed.

Results

The mean errors from 10-fold cross-validation showed a better accuracy level for the regression neural network model according to the long short-term memory model. The mean error between the DL-predicted skull shapes and CT-based skull shapes ranges from 1.67 mm to 3.99 mm by using the regression deep learning model and the best learning configuration. The volume deviation between predicted skull shapes and CT-based skull shapes is smaller than 5%.

Conclusions

The present study suggested that regression deep learning model allows human skull to be predicted from a given head with a good level of accuracy. This opens new avenues for the rapid generation of human skull shape from visual sensors (e.g. Microsoft Kinect) toward a computer-aided vision system for facial mimic rehabilitation. As perspectives, muscle network will be incorporated into the present workflow. Then, facial mimic movements will be tracked and animated to evaluate and optimize the rehabilitation movements and exercises.

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来源期刊
Irbm
Irbm ENGINEERING, BIOMEDICAL-
CiteScore
10.30
自引率
4.20%
发文量
81
审稿时长
57 days
期刊介绍: IRBM is the journal of the AGBM (Alliance for engineering in Biology an Medicine / Alliance pour le génie biologique et médical) and the SFGBM (BioMedical Engineering French Society / Société française de génie biologique médical) and the AFIB (French Association of Biomedical Engineers / Association française des ingénieurs biomédicaux). As a vehicle of information and knowledge in the field of biomedical technologies, IRBM is devoted to fundamental as well as clinical research. Biomedical engineering and use of new technologies are the cornerstones of IRBM, providing authors and users with the latest information. Its six issues per year propose reviews (state-of-the-art and current knowledge), original articles directed at fundamental research and articles focusing on biomedical engineering. All articles are submitted to peer reviewers acting as guarantors for IRBM''s scientific and medical content. The field covered by IRBM includes all the discipline of Biomedical engineering. Thereby, the type of papers published include those that cover the technological and methodological development in: -Physiological and Biological Signal processing (EEG, MEG, ECG…)- Medical Image processing- Biomechanics- Biomaterials- Medical Physics- Biophysics- Physiological and Biological Sensors- Information technologies in healthcare- Disability research- Computational physiology- …
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