Neural parameters estimation for brain tumor growth modeling
| Autor: | Emmanuel L. Barbier, Bjoern H. Menze, Nora Collomb, Ivan Ezhov, Suprosanna Shit, Florian Kofler, Benjamin Lemasson, Jana Lipkova |
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| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Machine Learning Computer science Quantitative Biology::Tissues and Organs Posterior probability Physics::Medical Physics Brain tumor Machine Learning (stat.ML) Quantitative Biology - Quantitative Methods Machine Learning (cs.LG) 030218 nuclear medicine & medical imaging Image (mathematics) Set (abstract data type) 03 medical and health sciences 0302 clinical medicine Statistics - Machine Learning FOS: Electrical engineering electronic engineering information engineering Medical imaging medicine Tumor growth Quantitative Methods (q-bio.QM) Partial differential equation Image and Video Processing (eess.IV) Electrical Engineering and Systems Science - Image and Video Processing medicine.disease Constraint (information theory) Tumor progression FOS: Biological sciences 030220 oncology & carcinogenesis Algorithm |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030322441 MICCAI (2) Lecture Notes in Computer Science Lecture Notes in Computer Science-Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 Medical Image Computing and Computer Assisted Intervention – MICCAI 2019-22nd International Conference, Shenzhen, China, October 13–17, 2019, Proceedings, Part II |
| ISSN: | 0302-9743 1611-3349 |
| Popis: | Understanding the dynamics of brain tumor progression is essential for optimal treatment planning. Cast in a mathematical formulation, it is typically viewed as evaluation of a system of partial differential equations, wherein the physiological processes that govern the growth of the tumor are considered. To personalize the model, i.e. find a relevant set of parameters, with respect to the tumor dynamics of a particular patient, the model is informed from empirical data, e.g., medical images obtained from diagnostic modalities, such as magnetic-resonance imaging. Existing model-observation coupling schemes require a large number of forward integrations of the biophysical model and rely on simplifying assumption on the functional form, linking the output of the model with the image information. In this work, we propose a learning-based technique for the estimation of tumor growth model parameters from medical scans. The technique allows for explicit evaluation of the posterior distribution of the parameters by sequentially training a mixture-density network, relaxing the constraint on the functional form and reducing the number of samples necessary to propagate through the forward model for the estimation. We test the method on synthetic and real scans of rats injected with brain tumors to calibrate the model and to predict tumor progression. |
| Databáze: | OpenAIRE |
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