Abstract
Cardiac four-chamber volumes provide crucial information for quantitative analysis of whole heart functions. Conventional cardiac volume estimation relies on a segmentation step; recently emerging direct estimation without segmentation has shown better performance than than segmentation-based methods. However, due to the high complexity, four-chamber volume estimation poses great challenges to these existing methods: four-chamber segmentation is not feasible due to intensity homogeneity of ventricle and atrium without implicit boundaries between them; existing direct methods which can only handle single or bi-ventricles are not directly applicable due to great combinatorial variability of four chambers. In this paper, by leveraging the full strength of direct estimation, we propose a new method for direct and simultaneous four-chamber volume estimation using multi-output regression that can disentangle complex relationship of image appearance and four-chamber volumes via statistical learning. To accomplish accurate and efficient estimation, we propose using a supervised descriptor learning (SDL) algorithm to generate a compact and discriminative feature representation. By casting into generalized low-rank approximations of matrices with a supervised manifold regularization, the SDL jointly removes irrelevant and redundant information by feature reduction and extracts discriminative features directly related to four chambers via supervised learning, which overcomes the high complexity of four chambers. We evaluate the proposed method on a cardiac four-chamber MR dataset from 125 subjects including both healthy and diseased cases. The experimental results show that our method achieves a high correlation coefficient of up to 91.5% with manual segmentation obtained by human experts. Our method for the first time achieves simultaneous and direct four-chamber volume estimation, which enables more efficient and accurate functional assessment of the whole heart.
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Zhen, X., Islam, A., Bhaduri, M., Chan, I., Li, S. (2015). Direct and Simultaneous Four-Chamber Volume Estimation by Multi-Output Regression. In: Navab, N., Hornegger, J., Wells, W., Frangi, A. (eds) Medical Image Computing and Computer-Assisted Intervention -- MICCAI 2015. MICCAI 2015. Lecture Notes in Computer Science(), vol 9349. Springer, Cham. https://doi.org/10.1007/978-3-319-24553-9_82
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