Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons

Efficient techniques for soft tissue modeling and simulation

Performing realistic deformation simulations in real time is a challenging problem in computer graphics. Among numerous proposed methods including Finite Element Modeling and ChainMail, we have implemented a mass spring system because of its acceptable accuracy and speed. Mass spring systems have, however, some drawbacks such as, the determination of simulation coefficients with their iterative nature. Given the correct parameters, mass spring systems can accurately simulate tissue deformations but choosing parameters that capture nonlinear deformation behavior is extremely difficult. Since most of the applications require a large number of elements i. e. points and springs in the modeling process it is extremely difficult to reach realtime performance with an iterative method. We have developed a new parameter identification method based on neural networks. The structure of the mass spring system is modified and neural networks are integrated into this structure. The input space consists of changes in spring lengths and velocities while a "teacher" signal is chosen as the total spring force, which is expressed in terms of positional changes and applied external forces. Neural networks are trained to learn nonlinear tissue characteristics represented by spring stiffness and damping in the mass spring algorithm. The learning algorithm is further enhanced by an adaptive learning rate, developed particularly for mass spring systems. In order to avoid the iterative approach in deformation simulations we have developed a new deformation algorithm. This algorithm defines the relationships between points and springs and specifies a set of rules on spring movements and deformations. These rules result in a deformation surface, which is called the search space. The deformation algorithm then finds the deformed points and springs in the search space with the help of the defined rules. The algorithm also sets rules on each element i. e. triangle or tetrahedron so that they do not pass through each other. The new algorithm is considerably faster than the original mass spring systems algorithm and provides an opportunity for various deformation applications. We have used mass spring systems and the developed method in the simulation of craniofacial surgery. For this purpose, a patient-specific head model was generated from MRI medical data by applying medical image processing tools such as, filtering, the segmentation and polygonal representation of such model is obtained using a surface generation algorithm. Prism volume elements are generated between the skin and bone surfaces so that different tissue layers are included to the head model. Both methods produce plausible results verified by surgeons.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Perception of multigrade classes schools: sociology of personalized learning

There are many schools of multigrade classes in many cities in Turkey due to the fact that there are various reasons, from a lack of financial budget to a harsh geographic demographic condition. Nevertheless, what should not be forgotten that this schooling is neither good nor bad, or is it neutral. Rather what the important is how they are utilized and how they came to be in the present practices in order to provide particular suggestions to authorities. To achieve the desired educational outcome of effectiveness and efficiency in these schools, the teachers' perception of these schooling is one of the crucial influencers. Therefore, this study aims to investigate how candidates of classroom teachers perceive the educational practices, in particular after visiting several of multigrade classes' schools through a participatory research approach. This study actually introduces the notions of education and sociology; particularly personalized learning in which the aim of progressive education approach ought to devalue political relativism and educational centralization and to revalue educational subjectivism and humanitarian utilitarianism. Particularly, this study aims to clarify the issue of responsibility and accountability in this schooling, and why and how this personalization of progressive education would be approached by whom

Social Exclusion and Ethnic Segregation in Schools: The Role of Teacher's Ethnic Prejudice

This is the author accepted manuscript. the final version is available from MIT Press via the DOI in this recordUsing data on primary school children and their teachers, we show that teachers who hold prejudicial attitudes towards an ethnic group create socially and spatially segregated classrooms. Leveraging a natural experiment where newly arrived refugee children are randomly assigned to teachers within schools, we find that teachers’ ethnic prejudice, measured by an implicit association test, significantly lowers the prevalence of inter-ethnic social links, increases homophilic ties among host children, and puts refugee children at a higher risk of peer violence. Our results highlight the role of teachers in achieving integrated schools in a world of increasing ethnic diversity.UK Foreign, Commonwealth & Development Offic

Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations

[EN] The development of accurate real-time models of the biomechanical behavior of different organs and tissues still poses a challenge in the field of biomechanical engineering. In the case of the liver, specifically, such a model would constitute a great leap forward in the implementation of complex applications such as surgical simulators, computed-assisted surgery or guided tumor irradiation. In this work, a relatively novel approach for developing such a model is presented. It consists in the use of a machine learning algorithm, which provides real-time inference, trained on tens of thousands of simulations of the biomechanical behavior of the liver carried out by the finite element method on more than 100 different liver geometries. Considering a target accuracy threshold of 3 mm for the Euclidean Error, four different scenarios were modeled and assessed: a single liver with an arbitrary force applied (99.96% of samples within the accepted error range), a single liver with two simultaneous forces applied (99.84% samples in range), a single liver with different material properties and an arbitrary force applied (98.46% samples in range), and a much more general model capable of modeling the behavior of any liver with an arbitrary force applied (99.01% samples in range for the median liver). The results show that the Machine Learning models perform extremely well on all the scenarios, managing to keep the Mean Euclidean Error under 1 mm in all cases. Furthermore, the proposed model achieves working frequencies above 100Hz on modest hardware (with frequencies above 1000Hz being easily achievable on more powerful GPUs) thus fulfilling the real-time requirements. These results constitute a remarkable improvement in this field and may involve a prompt implementation in clinical practice.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R, also supported by European FEDER funds.Pellicer-Valero, OJ.; Rupérez Moreno, MJ.; Martinez-Sanchis, S.; Martín-Guerrero, JD. (2020). Real-time biomechanical modeling of the liver using Machine Learning models trained on Finite Element Method simulations. Expert Systems with Applications. 143:1-12. https://doi.org/10.1016/j.eswa.2019.113083S112143Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., Kudlur, M., Levenberg, J., Monga, R., Moore, S., Murray, D. G., Steiner, B., Tucker, P., Vasudevan, V., Warden, P., Wicke, M., Yu, Y., & Zheng, X. (2016). TensorFlow: A system for large-scale machine learning. arXiv:1605.08695.Brunon, A., Bruyère-Garnier, K., & Coret, M. (2010). Mechanical characterization of liver capsule through uniaxial quasi-static tensile tests until failure. Journal of Biomechanics, 43(11), 2221-2227. doi:10.1016/j.jbiomech.2010.03.038Chinesta, F., Leygue, A., Bordeu, F., Aguado, J. V., Cueto, E., Gonzalez, D., … Huerta, A. (2013). PGD-Based Computational Vademecum for Efficient Design, Optimization and Control. Archives of Computational Methods in Engineering, 20(1), 31-59. doi:10.1007/s11831-013-9080-xClifford, M. A., Banovac, F., Levy, E., & Cleary, K. (2002). Assessment of Hepatic Motion Secondary to Respiration for Computer Assisted Interventions. Computer Aided Surgery, 7(5), 291-299. doi:10.3109/10929080209146038Cotin, S., Delingette, H., & Ayache, N. (2000). A hybrid elastic model for real-time cutting, deformations, and force feedback for surgery training and simulation. The Visual Computer, 16(8), 437-452. doi:10.1007/pl00007215Duysak, A., Zhang, J. J., & Ilankovan, V. (2003). Efficient modelling and simulation of soft tissue deformation using mass-spring systems. International Congress Series, 1256, 337-342. doi:10.1016/s0531-5131(03)00423-0Fung, Y. C., & Skalak, R. (1981). Biomechanics: Mechanical Properties of Living Tissues. Journal of Biomechanical Engineering, 103(4), 231-298. doi:10.1115/1.3138285González, D., Aguado, J. V., Cueto, E., Abisset-Chavanne, E., & Chinesta, F. (2016). kPCA-Based Parametric Solutions Within the PGD Framework. Archives of Computational Methods in Engineering, 25(1), 69-86. doi:10.1007/s11831-016-9173-4González, D., Cueto, E., & Chinesta, F. (2015). Computational Patient Avatars for Surgery Planning. Annals of Biomedical Engineering, 44(1), 35-45. doi:10.1007/s10439-015-1362-zJahya, A., Herink, M., & Misra, S. (2013). A framework for predicting three-dimensional prostate deformation in real time. The International Journal of Medical Robotics and Computer Assisted Surgery, 9(4), e52-e60. doi:10.1002/rcs.1493Lister, K., Gao, Z., & Desai, J. P. (2010). Development of In Vivo Constitutive Models for Liver: Application to Surgical Simulation. Annals of Biomedical Engineering, 39(3), 1060-1073. doi:10.1007/s10439-010-0227-8Lorente, D., Martínez-Martínez, F., Rupérez, M. J., Lago, M. A., Martínez-Sober, M., Escandell-Montero, P., … Martín-Guerrero, J. D. (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications, 71, 342-357. doi:10.1016/j.eswa.2016.11.037Maas, S. A., Ellis, B. J., Ateshian, G. A., & Weiss, J. A. (2012). FEBio: Finite Elements for Biomechanics. Journal of Biomechanical Engineering, 134(1). doi:10.1115/1.4005694Myronenko, A., & Xubo Song. (2010). Point Set Registration: Coherent Point Drift. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(12), 2262-2275. doi:10.1109/tpami.2010.46Niroomandi, S., Alfaro, I., Cueto, E., & Chinesta, F. (2012). Accounting for large deformations in real-time simulations of soft tissues based on reduced-order models. Computer Methods and Programs in Biomedicine, 105(1), 1-12. doi:10.1016/j.cmpb.2010.06.012Plantefève, R., Peterlik, I., Haouchine, N., & Cotin, S. (2015). Patient-Specific Biomechanical Modeling for Guidance During Minimally-Invasive Hepatic Surgery. Annals of Biomedical Engineering, 44(1), 139-153. doi:10.1007/s10439-015-1419-zLarge elastic deformations of isotropic materials. I. Fundamental concepts. (1948). Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 240(822), 459-490. doi:10.1098/rsta.1948.0002Large elastic deformations of isotropic materials IV. further developments of the general theory. (1948). Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, 241(835), 379-397. doi:10.1098/rsta.1948.0024Ruder, S. (2016). An overview of gradient descent optimization algorithms. (pp. 1–14). arXiv: 1609.04747.Untaroiu, C. D., & Lu, Y.-C. (2013). Material characterization of liver parenchyma using specimen-specific finite element models. Journal of the Mechanical Behavior of Biomedical Materials, 26, 11-22. doi:10.1016/j.jmbbm.2013.05.013Valanis, K. C., & Landel, R. F. (1967). The Strain‐Energy Function of a Hyperelastic Material in Terms of the Extension Ratios. Journal of Applied Physics, 38(7), 2997-3002. doi:10.1063/1.171003

A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning

Progress in biomechanical modelling of human soft tissue is the basis for the development of new clinical applications capable of improving the diagnosis and treatment of some diseases (e.g. cancer), as well as the surgical planning and guidance of some interventions. The finite element method (FEM) is one of the most popular techniques used to predict the deformation of the human soft tissue due to its high accuracy. However, FEM has an associated high computational cost, which makes it difficult its integration in real-time computer-aided surgery systems. An alternative for simulating the mechanical behaviour of human organs in real time comes from the use of machine learning (ML) techniques, which are much faster than FEM. This paper assesses the feasibility of ML methods for modelling the biomechanical behaviour of the human liver during the breathing process, which is crucial for guiding surgeons during interventions where it is critical to track this deformation (e.g. some specific kind of biopsies) or for the accurate application of radiotherapy dose to liver tumours. For this purpose, different ML regression models were investigated, including three tree-based methods (decision trees, random forests and extremely randomised trees) and other two simpler regression techniques (dummy model and linear regression). In order to build and validate the ML models, a labelled data set was constructed from modelling the deformation of eight ex-vivo human livers using FEM. The best prediction performance was obtained using extremely randomised trees, with a mean error of 0.07 mm and all the samples with an error under 1 mm. The achieved results lay the foundation for the future development of some real-time software capable of simulating the human liver deformation during the breathing process during clinical interventions.This work has been funded by the Spanish Ministry of Economy and Competitiveness (MINECO) through research projects TIN2014-52033-R and DPI2013-40859-R, both also supported by European FEDER funds. The authors acknowledge the kind collaboration of the personnel from the hospital involved in the research.Lorente, D.; Martínez-Martínez, F.; Rupérez Moreno, MJ.; Lago, MA.; Martínez-Sober, M.; Escandell-Montero, P.; Martínez-Martínez, JM.... (2017). A framework for modelling the biomechanical behaviour of the human liver during breathing in real time using machine learning. Expert Systems with Applications. 71:342-357. doi:10.1016/j.eswa.2016.11.037S3423577

GAZÂLÎ’NİN NEDENSELLİK ANLAYIŞI VE KUANTUM FİZİĞİ KOPENHAG YORUMU BELİRSİZLİK İLKESİNİN KARŞILAŞTIRILMASI

İslâm düşüncesinde önemli bir yeri olan Gazâlî nedensellik anlayışı ile çağının ötesine uzanan etkiler bırakmıştır. Gazâlî’nin nedensellik anlayışının modern fizikle olan benzerlik ve farklılıklarını ortaya çıkartmayı amaçladığımız tezimizde konuyu kavramsal düzeyde ele aldık. Hareket kavramı bağlamında incelediğimiz nedenselliğin zorunluluğu meselesinin, süreklilik ve süreksizlik düşüncelerinin ekseninde değiştiğini göstermeye çalıştık. Gazâlî örneği üzerinden incelediğimiz nedenselliğin felsefi temellerinin ortaya çıkartılması suretiyle bilimin cevap aradığı sorulara olgusal yaklaşım kadar kavramsal düzeyde de yaklaşılmasının gerekliliğini göstermeyi amaçlıyoruz. Bu sayede felsefe ile bilimin birbirlerini destekleyerek gelişmeleri gerektiğini ortaya koymayı hedefliyoruz. Araştırmamız dört bölümden oluşmaktadır. Birinci bölümde ilkçağ filozoflarından Gazâlî’ye kadar geçen süre içerisinde neden ve nedensellik kavramlarının nasıl ele alındığı incelenmiştir. İkinci bölümde Gazâlî’nin nedensellik düşüncesi ve buna karşı yöneltilen eleştiriler açıklanmıştır. Üçüncü bölümde kuantum fiziği Kopenhag yorumunun gelişimi Newton fiziğinden itibaren ele alınarak incelenmiştir. Son bölümde belirsizlik ilkesinin temel esasları açıklanarak Gazâlî’nin nedensellik anlayışıyla ortak ve farklı yönleri incelenmiştir. Bu kısmın sonunda yaptığımız genel değerlendirmeyle Gazâlî’nin nedensellik anlayışının geliştirilerek modern fiziğin ihtiyaç duyduğu yeni bir epistemoloji inşasına katkıda bulunulabileceğine dikkat çekmeye çalıştık

Akkuyu Sahilleri (Mersin-Doğu Akdeniz) cephalopod

TEZ4292Tez (Yüksek Lisans) -- Çukurova Üniversitesi, Adana, 2002.Kaynakça (s. 56-58) var.vi, 58 s. ; 30 cm.…Bu çalışma Ç.Ü. Bilimsel Araştırma Projeleri Birimi Tarafından Desteklenmiştir. Proje No:FBE.YL.2000.14