Abstract
This paper proposes the computing of isosurfaces as a way to extract relevant features from 3D brain images. These isosurfaces are then used to implement a Computer aided diagnosis system to assist in the diagnosis of Parkinson’s Disease (PD) which uses a most well-known Convolutional Neural Networks (CNN) architecture, LeNet, to classify DaTScan images with an average accuracy of 95.1% and AUC = 97%, obtaining comparable (slightly better) values to those obtained for most of the recently proposed systems. It can be concluded therefore that the computation of isosurfaces reduces the complexity of the inputs significantly, resulting in high classification accuracies with reduced computational burden.
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Acknowledgments
This work was partly supported by the MINECO/FEDER under TEC2015-64718-R and PSI2015-65848-R projects. We gratefully acknowledge the support of NVIDIA Corporation with the donation of one of the GPUs used for this research. PPMI - a public - private partnership - is funded by The Michael J. Fox Foundation for Parkinson’s Research and funding partners, including Abbott, Biogen Idec, F. Hoffman-La Roche Ltd., GE Healthcare, Genentech and Pfizer Inc.
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Martínez-Ibañez, M., Ortiz, A., Munilla, J., Salas-Gonzalez, D., Górriz, J.M., Ramírez, J. (2019). Isosurface Modelling of DatSCAN Images for Parkinson Disease Diagnosis. In: Ferrández Vicente, J., Álvarez-Sánchez, J., de la Paz López, F., Toledo Moreo, J., Adeli, H. (eds) Understanding the Brain Function and Emotions. IWINAC 2019. Lecture Notes in Computer Science(), vol 11486. Springer, Cham. https://doi.org/10.1007/978-3-030-19591-5_37
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