Gaussian process with physical laws for 3D cardiac modeling
Cardiovascular disease is one of the leading causes of both morbidity and mortality all over the world. Early diagnosis and treatment planning are demanded for the wide variety of etiologies and pathophysiologies. In the last decades, intensive research in the field of computational biology has demonstrated the potential ability of three-dimensional (3D) cardiac computational models to give us a clue to perform early diagnosis or to have high affinity with machine learning for treatment planning. We introduces some physical laws into a Gaussian process for a statistical 3D cardiac computational model. The heart shape must be ruled by some physical laws, which should be an important clue for the statistical shape estimation. For demonstration, we apply our model into the pipeline that estimates the heart shape from cardiovascular magnetic resonance (CMR) imaging, by combining it with the deep neural networks-based anatomical segmentation of CMR imaging.
M. Nakano, R. Shibue, K. Kashino, S. Tsukada, H. Tomoike, “Gaussian process with physical laws for 3D cardiac modeling,” under review.
T. Ngo, Z. Lu, G. Carneiro, “Combining deep learning and level set for the automated segmentation of the left ventricle of the heart from cardiac cine magnetic resonance,” Medical Image Analysis, Vol. 35, pp. 159–171, 2019.