Abstract
Left ventricle full quantification is important in the assessment of cardiac functionality and diagnosis of cardiac diseases, but is also challenging due to the sample variability and label correlations. In this paper, we propose a deep-learning based approach for left ventricle full quantification, including 11 indices regression and cardiac phase recognition. We utilize Deep Layer Aggregation as backbone, perform 11 indices regression simultaneously supervised by multitask relationship loss, and then derive the cardiac phase by searching maximum and minimum frame from polynomial-fitted cavity area. Experiments demonstrate the superiority of the proposed method in performance.
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Li, J., Hu, Z. (2019). Left Ventricle Full Quantification Using Deep Layer Aggregation Based Multitask Relationship 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_41
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DOI: https://doi.org/10.1007/978-3-030-12029-0_41
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