Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method_Journal of Biomedical Engineering

Authors：

 LI Haiyan ^1,2 , LI Haifang ^1,2 , HE Guanglong ³ , LIU Wengang ^1,2 , CUI Shihai ^1,2 , HE Lijuan ^1,2 , LU Wenle ^1,2 , PAN Jianyu ^1,2 , ZHOU Yiwu ⁴

1. International Research Association on Emerging Automotive Safety Technology, Tianjin 300222, P. R. China;
2. School of Mechanical Engineering, Tianjin University of Science and Technology, Tianjin 300222, P. R. China;
3. Institute of Forensic Science, Ministry of Public Security, Beijing 100038, P. R. China;
4. Department of Forensic Medicine, Tongji Medical College of Huazhong University of Science and Technology, Wuhan 430010, P. R. China;

Corresponding author：

LI Haiyan, Email: lihaiyan@tust.edu.cn

Keywords：

Convolutional neural network; Finite element analysis; Local brain injury; Stick blunt

DOI：

10.7507/1001-5515.202106087

Video：

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The finite element method is a new method to study the mechanism of brain injury caused by blunt instruments. But it is not easy to be applied because of its technology barrier of time-consuming and strong professionalism. In this study, a rapid and quantitative evaluation method was investigated to analyze the craniocerebral injury induced by blunt sticks based on convolutional neural network and finite element method. The velocity curve of stick struck and the maximum principal strain of brain tissue (cerebrum, corpus callosum, cerebellum and brainstem) from the finite element simulation were used as the input and output parameters of the convolutional neural network The convolutional neural network was trained and optimized by using the 10-fold cross-validation method. The Mean Absolute Error (MAE), Mean Square Error (MSE), and Goodness of Fit (R²) of the finally selected convolutional neural network model for the prediction of the maximum principal strain of the cerebrum were 0.084, 0.014, and 0.92, respectively. The predicted results of the maximum principal strain of the corpus callosum were 0.062, 0.007, 0.90, respectively. The predicted results of the maximum principal strain of the cerebellum and brainstem were 0.075, 0.011, and 0.94, respectively. These results show that the research and development of the deep convolutional neural network can quickly and accurately assess the local brain injury caused by the sticks blow, and have important application value for understanding the quantitative evaluation and the brain injury caused by the sticks struck. At the same time, this technology improves the computational efficiency and can provide a basis reference for transforming the current acceleration-based brain injury research into a focus on local brain injury research.

Citation： LI Haiyan, LI Haifang, HE Guanglong, LIU Wengang, CUI Shihai, HE Lijuan, LU Wenle, PAN Jianyu, ZHOU Yiwu. Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method. Journal of Biomedical Engineering, 2022, 39(2): 276-284. doi: 10.7507/1001-5515.202106087 Copy

1.	Yoganandan N, Pintar F A, Sances A J, et al. Biomechanics of skull fracture. J Neurotrauma, 1995, 12(4): 659-668.
2.	Trosseille X, Tarriere C, Lavaste F, et al. Development of a FEM of the human head according to a specific test protocol, 36 th Stapp CarCrash Conference, 1992: 235-253.
3.	Hayward P. Traumatic brain injury: the signature of modern conflicts. Lancet Neurol, 2008, 7(3): 200-201.
4.	Bellander B M, Olafsson I H, Ghatan P H, et al. Secondary insults following traumatic brain injury enhance complement activation in the human brain and release of the tissue damage marker S100B. Acta Neurochir, 2011, 153(1): 90-100.
5.	Jeter C B, Hergenroeder G W, Hylin M J, et al. Biomarkers for the diagnosis and prognosis of mild traumatic brain injury/concussion. J Neurotrauma, 2013, 30(8): 657-670.
6.	陈越, 崔世海, 李海岩, 等. 几何尺寸和材料特性对头部转动特性的影响. 生物医学工程学杂志, 2016, 33(4): 639-644.
7.	Li K, Wang J W, Liu S X, et al. Biomechanical behavior of brain injury caused by sticks using finite element model and hybrid-III testing. Chin J Traumatol, 2015, 18(2): 65-73.
8.	Franklyn M, Fildes B, Zhang L, et al. Analysis of finite element models for head injury investigation: reconstruction of four real-world impacts. Stapp Car Crash J, 2005, 49: 1-32.
9.	Takhounts E G, Ridella S A, Hasija V, et al. Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. Stapp Car Crash J, 2008, 52: 1-31.
10.	Gabler L F, Joodaki H, Crandall J R, et al. Development of a single-degree-of-freedom mechanical model for predicting strain-based brain injury responses. J Biomech Eng, 2018, 140(3): 031002.
11.	Laksari K, Wu L C, Kurt M, et al. Resonance of human brain under head acceleration. J R Soc Interface, 2015, 12: 20150331.
12.	Gabler L F, Crandall J R, Panzer M B. Development of a second-order system for rapid estimation of maximum brain strain. Ann Biomed Eng, 2019, 47(9): 1971-1981.
13.	Prasad P, Mertz H J. The position of the United States delegation to the ISO working group 6 on the use of HIC in the automotive environment, SAE transactions, 1985: 106-116.
14.	Newman J A, Shewchenko N, Welbourne E. A proposed new biomechanical head injury assessment function - the maximum power index. Stapp Car Crash J, 2000, 44: 215-247.
15.	Newman J A. A generalized acceleration model for brain injury threshold (GAMBIT). Proceedings of IRCOBI Conference, 1986: 121-131.
16.	Wu S, Zhao W, Ghazi K, et al. Convolutional neural network for efficient estimation of regional brain strains. Sci Rep, 2019, 9: 17326.
17.	Rane L, Ding Z, Mcgregor A H, et al. Deep learning for musculoskeletal force prediction. Ann Biomed Eng, 2019, 47(3): 778-789.
18.	Hannink J, Kautz T, Pasluosta C F, et al. Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE J Biomed Health Inform, 2017, 21(1): 85-93.
19.	Daimary D, Bora M B, Amitab K, et al. Brain tumor segmentation from MRI images using hybrid convolutional neural networks. Procedia Computer Science, 2020, 167: 2419-2428.
20.	Taigman Y, Yang Ming, Ranzato M A, et al. DeepFace: closing the gap to human-level performance in face verification//2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2014: 1701-1708.
21.	Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137-1149.
22.	Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, 2012, 25: 1097-1105.
23.	刘一鸣, 侯智超, 李晓琴, 等. 基于卷积神经网络的肺结节检测方法. 生物医学工程学杂志, 2019, 36(6): 969-977, 985.
24.	Pang Yanwei, Sun Manli, Jiang Xiaoheng, et al. Convolution in convolution for network in network. IEEE Trans Neural Netw Learn Syst, 2017, 29(5): 1587-1597.
25.	何培. 三维人体颅脑有限元模型构建和颅脑正面碰撞分析. 天津: 天津科技大学, 2006.
26.	Nahum A M, Smith R W. An experimental model for closed head impact injury//Proceedings of 20th Stapp Car Crash Conference, 1976: 783-814.
27.	Nahum A M, Smith R, Ward C C. Intracranial pressure dynamics during head impact//Proceedings of 21st Stapp Car Crash Conference, 1977: 339-366.
28.	Yoganandan N, Zhang J, Pintar F. Force and acceleration corridors from lateral head impact. Traffic Inj Prev, 2004, 5(4): 368-373.
29.	Koenig K, Mitchell N D, Hannigan T E, et al. The influence of moment of inertia on baseball/softball bat swing speed. Sports Engineering, 2004, 7(2): 105-117.
30.	Koike S, Mimura K. Main contributors to the baseball bat head speed considering the generating factor of motion-dependent term. Procedia Engineering, 2016, 147: 197-202.
31.	Thibault L E. Brain injury from the macro to the micro level and back again: what have we learned to date?. Proceedings of the 1993 International IRCOBI Conference on the Biomechanics of Impact, 1993: 3-25.
32.	Takhounts E G, Craig M J, Moorhouse K, et al. Development of brain injury criteria (BrIC). Stapp Car Crash Journal, 2013, 57: 243-266.
33.	Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal, 2017, 35: 18-31.
34.	Szegedy C, Liu Wei, Jia Yangqing, et al. Going deeper with convolutions//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015. arXiv: 1409.4842.
35.	Shi Guang, Zhang Jiangshe, Li Huirong, et al. Enhance the performance of deep neural networks via L2 regularization on the input of activations. Neural Process Lett, 2019, 50(1): 57-75.
36.	Krstajic D, Buturovic L J, Leahy D, et al. Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform, 2014, 6(1): 10.
37.	Pillonetto G, Nicolao G D. Pitfalls of the parametric approaches exploiting cross-validation for model order selection. IFAC Proceedings Volumes, 2012, 45(16): 215-220.
38.	Ji S, Zhao W. A pre-computed brain response atlas for instantaneous strain estimation in contact sports. Ann Biomed Eng, 2015, 43(8): 1877-1895.
39.	Pak W, Meng Yunzhu, Schap J, et al. Finite element model of a high-stature male pedestrian for simulating car-to-pedestrian collisions. International Journal of Automotive Technology, 2019, 20(3): 445-453.
40.	Zhao W, Ruan S, Li H, et al. Development and validation of a 5th percentile human head finite element model based on the Chinese population. Int J Veh Saf, 2012, 6(2): 91-109.
41.	Li H, Li K, Huang Y, et al. Validation of a finite element model with six-year-old child anatomical characteristics as specified in Euro NCAP pedestrian human model certification (TB024). Computer Methods in Biomechanics and Biomedical Engineering, 2020, 2020(6): 1-15.
42.	Meng Y, Pak W, Guleyupoglu B, et al. A finite element model of a six-year-old child for simulating pedestrian accidents. Accid Anal Prev, 2017, 98: 206-213.

1. Yoganandan N, Pintar F A, Sances A J, et al. Biomechanics of skull fracture. J Neurotrauma, 1995, 12(4): 659-668.
2. Trosseille X, Tarriere C, Lavaste F, et al. Development of a FEM of the human head according to a specific test protocol, 36 th Stapp CarCrash Conference, 1992: 235-253.
3. Hayward P. Traumatic brain injury: the signature of modern conflicts. Lancet Neurol, 2008, 7(3): 200-201.
4. Bellander B M, Olafsson I H, Ghatan P H, et al. Secondary insults following traumatic brain injury enhance complement activation in the human brain and release of the tissue damage marker S100B. Acta Neurochir, 2011, 153(1): 90-100.
5. Jeter C B, Hergenroeder G W, Hylin M J, et al. Biomarkers for the diagnosis and prognosis of mild traumatic brain injury/concussion. J Neurotrauma, 2013, 30(8): 657-670.
6. 陈越, 崔世海, 李海岩, 等. 几何尺寸和材料特性对头部转动特性的影响. 生物医学工程学杂志, 2016, 33(4): 639-644.
7. Li K, Wang J W, Liu S X, et al. Biomechanical behavior of brain injury caused by sticks using finite element model and hybrid-III testing. Chin J Traumatol, 2015, 18(2): 65-73.
8. Franklyn M, Fildes B, Zhang L, et al. Analysis of finite element models for head injury investigation: reconstruction of four real-world impacts. Stapp Car Crash J, 2005, 49: 1-32.
9. Takhounts E G, Ridella S A, Hasija V, et al. Investigation of traumatic brain injuries using the next generation of simulated injury monitor (SIMon) finite element head model. Stapp Car Crash J, 2008, 52: 1-31.
10. Gabler L F, Joodaki H, Crandall J R, et al. Development of a single-degree-of-freedom mechanical model for predicting strain-based brain injury responses. J Biomech Eng, 2018, 140(3): 031002.
11. Laksari K, Wu L C, Kurt M, et al. Resonance of human brain under head acceleration. J R Soc Interface, 2015, 12: 20150331.
12. Gabler L F, Crandall J R, Panzer M B. Development of a second-order system for rapid estimation of maximum brain strain. Ann Biomed Eng, 2019, 47(9): 1971-1981.
13. Prasad P, Mertz H J. The position of the United States delegation to the ISO working group 6 on the use of HIC in the automotive environment, SAE transactions, 1985: 106-116.
14. Newman J A, Shewchenko N, Welbourne E. A proposed new biomechanical head injury assessment function - the maximum power index. Stapp Car Crash J, 2000, 44: 215-247.
15. Newman J A. A generalized acceleration model for brain injury threshold (GAMBIT). Proceedings of IRCOBI Conference, 1986: 121-131.
16. Wu S, Zhao W, Ghazi K, et al. Convolutional neural network for efficient estimation of regional brain strains. Sci Rep, 2019, 9: 17326.
17. Rane L, Ding Z, Mcgregor A H, et al. Deep learning for musculoskeletal force prediction. Ann Biomed Eng, 2019, 47(3): 778-789.
18. Hannink J, Kautz T, Pasluosta C F, et al. Sensor-based gait parameter extraction with deep convolutional neural networks. IEEE J Biomed Health Inform, 2017, 21(1): 85-93.
19. Daimary D, Bora M B, Amitab K, et al. Brain tumor segmentation from MRI images using hybrid convolutional neural networks. Procedia Computer Science, 2020, 167: 2419-2428.
20. Taigman Y, Yang Ming, Ranzato M A, et al. DeepFace: closing the gap to human-level performance in face verification//2014 IEEE Conference on Computer Vision and Pattern Recognition, IEEE Computer Society, 2014: 1701-1708.
21. Ren Shaoqing, He Kaiming, Girshick R, et al. Faster R-CNN: towards real-time object detection with region proposal networks. IEEE Trans Pattern Anal Mach Intell, 2017, 39(6): 1137-1149.
22. Krizhevsky A, Sutskever I, Hinton G. Imagenet classification with deep convolutional neural networks. Neural Information Processing Systems, 2012, 25: 1097-1105.
23. 刘一鸣, 侯智超, 李晓琴, 等. 基于卷积神经网络的肺结节检测方法. 生物医学工程学杂志, 2019, 36(6): 969-977, 985.
24. Pang Yanwei, Sun Manli, Jiang Xiaoheng, et al. Convolution in convolution for network in network. IEEE Trans Neural Netw Learn Syst, 2017, 29(5): 1587-1597.
25. 何培. 三维人体颅脑有限元模型构建和颅脑正面碰撞分析. 天津: 天津科技大学, 2006.
26. Nahum A M, Smith R W. An experimental model for closed head impact injury//Proceedings of 20th Stapp Car Crash Conference, 1976: 783-814.
27. Nahum A M, Smith R, Ward C C. Intracranial pressure dynamics during head impact//Proceedings of 21st Stapp Car Crash Conference, 1977: 339-366.
28. Yoganandan N, Zhang J, Pintar F. Force and acceleration corridors from lateral head impact. Traffic Inj Prev, 2004, 5(4): 368-373.
29. Koenig K, Mitchell N D, Hannigan T E, et al. The influence of moment of inertia on baseball/softball bat swing speed. Sports Engineering, 2004, 7(2): 105-117.
30. Koike S, Mimura K. Main contributors to the baseball bat head speed considering the generating factor of motion-dependent term. Procedia Engineering, 2016, 147: 197-202.
31. Thibault L E. Brain injury from the macro to the micro level and back again: what have we learned to date?. Proceedings of the 1993 International IRCOBI Conference on the Biomechanics of Impact, 1993: 3-25.
32. Takhounts E G, Craig M J, Moorhouse K, et al. Development of brain injury criteria (BrIC). Stapp Car Crash Journal, 2013, 57: 243-266.
33. Havaei M, Davy A, Warde-Farley D, et al. Brain tumor segmentation with deep neural networks. Med Image Anal, 2017, 35: 18-31.
34. Szegedy C, Liu Wei, Jia Yangqing, et al. Going deeper with convolutions//2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, 2015. arXiv: 1409.4842.
35. Shi Guang, Zhang Jiangshe, Li Huirong, et al. Enhance the performance of deep neural networks via L2 regularization on the input of activations. Neural Process Lett, 2019, 50(1): 57-75.
36. Krstajic D, Buturovic L J, Leahy D, et al. Cross-validation pitfalls when selecting and assessing regression and classification models. J Cheminform, 2014, 6(1): 10.
37. Pillonetto G, Nicolao G D. Pitfalls of the parametric approaches exploiting cross-validation for model order selection. IFAC Proceedings Volumes, 2012, 45(16): 215-220.
38. Ji S, Zhao W. A pre-computed brain response atlas for instantaneous strain estimation in contact sports. Ann Biomed Eng, 2015, 43(8): 1877-1895.
39. Pak W, Meng Yunzhu, Schap J, et al. Finite element model of a high-stature male pedestrian for simulating car-to-pedestrian collisions. International Journal of Automotive Technology, 2019, 20(3): 445-453.
40. Zhao W, Ruan S, Li H, et al. Development and validation of a 5th percentile human head finite element model based on the Chinese population. Int J Veh Saf, 2012, 6(2): 91-109.
41. Li H, Li K, Huang Y, et al. Validation of a finite element model with six-year-old child anatomical characteristics as specified in Euro NCAP pedestrian human model certification (TB024). Computer Methods in Biomechanics and Biomedical Engineering, 2020, 2020(6): 1-15.
42. Meng Y, Pak W, Guleyupoglu B, et al. A finite element model of a six-year-old child for simulating pedestrian accidents. Accid Anal Prev, 2017, 98: 206-213.

Journal of Biomedical Engineering

Evaluation of brain injury caused by stick type blunt instruments based on convolutional neural network and finite element method

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