Abstract
Left ventricle (LV) quantification is of great clinical importance for diagnosing and monitoring cardiac diseases. Full quantification of LV indices includes: (1) two areas of LV cavity and myocardium, (2) six regional wall thicknesses (RWT), (3) three LV dimensions, and (4) phase identification (diastole or systole). However, due to the large variability in the object shape and imaging quality, it is time-consuming and user-dependent to quantify LV parameters manually. In this work, we propose a cascading deep neural network, including an enhanced supervision U-net followed a recurrent neural network (RNN) type of phase-prediction net called P-net, abbreviated as ESU-P-net, for full LV quantification in a fully automated manner. The proposed ESU-P-net framework is dedicated to the full quantification of LV for all four types of indices.
Experiments on MR sequences of 145 subjects provided by MICCAI 2018 STACOM Challenge showed that the proposed network achieved highly accurate LV quantification, with an average mean absolute error (MAE) of 62 mm2, 1.14 mm, 0.96 mm for LV areas, RWT, dimensions, respectively, and an error rate of 8.0% for cardiac phase identification.
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Acknowledgement
This work was supported by National Key Research and Development Program of China (No. 2018YFC0116303).
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Yan, W., Wang, Y., Chen, S., van der Geest, R.J., Tao, Q. (2019). ESU-P-Net: Cascading Network for Full Quantification of Left Ventricle from Cine MRI. 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_45
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DOI: https://doi.org/10.1007/978-3-030-12029-0_45
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