{"title":"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","authors":"H.-Q. Nguyen , T.-N. Nguyen , V.-D. Tran , T.-T. Dao","doi":"10.1016/j.irbm.2022.05.005","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>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<span>. 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.</span></p></div><div><h3>Material and methods</h3><p>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.</p></div><div><h3>Results</h3><p>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%.</p></div><div><h3>Conclusions</h3><p>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.</p></div>","PeriodicalId":14605,"journal":{"name":"Irbm","volume":null,"pages":null},"PeriodicalIF":5.6000,"publicationDate":"2023-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A Deep Learning Approach for Predicting Subject-Specific Human Skull Shape from Head Toward a Decision Support System for Home-Based Facial Rehabilitation\",\"authors\":\"H.-Q. Nguyen , T.-N. Nguyen , V.-D. Tran , T.-T. Dao\",\"doi\":\"10.1016/j.irbm.2022.05.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Objective</h3><p>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<span>. 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.</span></p></div><div><h3>Material and methods</h3><p>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.</p></div><div><h3>Results</h3><p>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%.</p></div><div><h3>Conclusions</h3><p>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.</p></div>\",\"PeriodicalId\":14605,\"journal\":{\"name\":\"Irbm\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":5.6000,\"publicationDate\":\"2023-02-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Irbm\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1959031822000586\",\"RegionNum\":4,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, BIOMEDICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Irbm","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1959031822000586","RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, BIOMEDICAL","Score":null,"Total":0}
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.
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-
…