Abstract
Segmentation of the left ventricle and quantification of various cardiac contractile functions is crucial for the timely diagnosis and treatment of cardiovascular diseases. Traditionally, the two tasks have been tackled independently. Here we propose a convolutional neural network based multi-task learning approach to perform both tasks simultaneously, such that, the network learns better representation of the data with improved generalization performance. Probabilistic formulation of the problem enables learning the task uncertainties during the training, which are used to automatically compute the weights for the tasks. We performed a five fold cross-validation of the myocardium segmentation obtained from the proposed multi-task network on 97 patient 4-dimensional cardiac cine-MRI datasets available through the STACOM LV segmentation challenge against the provided gold-standard myocardium segmentation, obtaining a Dice overlap of \(0.849 \pm 0.036\) and mean surface distance of \(0.274 \pm 0.083\) mm, while simultaneously estimating the myocardial area with mean absolute difference error of \(205\pm 198\,\mathrm{mm}^2\).
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Acknowledgements
Research reported in this publication was supported by the National Institute of General Medical Sciences of the National Institutes of Health under Award No. R35GM128877 and by the Office of Advanced Cyberinfrastructure of the National Science Foundation under Award No. 1808530. Ziv Yaniv’s work was supported by the Intramural Research Program of the U.S. National Institutes of Health, National Library of Medicine.
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Dangi, S., Yaniv, Z., Linte, C.A. (2019). Left Ventricle Segmentation and Quantification from Cardiac Cine MR Images via Multi-task Learning. In: Pop, M., et al. Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges. STACOM 2018. Lecture Notes in Computer Science(), vol 11395. Springer, Cham. https://doi.org/10.1007/978-3-030-12029-0_3
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