TY - GEN
T1 - Multi-component deformable models coupled with 2D-3D U-Net for automated probabilistic segmentation of cardiac walls and blood
AU - Yang, Dong
AU - Huang, Qiaoying
AU - Axel, Leon
AU - Metaxas, Dimitris
N1 - Funding Information: * Authors contributed equally The authors would like to thank the NIH funding support received from grant number 1R01HL127661-01.
PY - 2018/5/23
Y1 - 2018/5/23
N2 - The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been investigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mixture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel approach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, a 2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi-component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, robustness, and efficiency of our approach for the slices acquired at different locations and different cardiac phases.
AB - The segmentation of the ventricular wall and the blood pool in cardiac magnetic resonance imaging (MRI) has been investigated for decades, given its important role for delineation of cardiac functioning and diagnosis of heart diseases. One of the major challenges is that the inner epicardium boundary is not always visible in the image domain, due to the mixture of blood and muscle structures, especially at the end of contraction, or systole. To address it, we propose a novel approach for the cardiac segmentation in the short-axis (SAX) MRI: coupled deep neural networks and deformable models. First, a 2D U-Net is adopted for each magnetic resonance (MR) slice, and a 3D U-Net refines the segmentation results along the temporal dimension. Then, we propose a multi-component deformable model to extract accurate contours for both endo- and epicardium with global and local constraints. Finally, a partial blood classification is explored to estimate the presence of boundary pixels near the trabeculae and solid wall, and to avoid moving the endocardium boundary inward. Quantitative evaluation demonstrates the high accuracy, robustness, and efficiency of our approach for the slices acquired at different locations and different cardiac phases.
KW - Cardiac MRI
KW - Multi-component deformable model
KW - Partial blood classification
KW - U-Net
KW - Ventricular wall segmentation
UR - http://www.scopus.com/inward/record.url?scp=85048090390&partnerID=8YFLogxK
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U2 - https://doi.org/10.1109/ISBI.2018.8363620
DO - https://doi.org/10.1109/ISBI.2018.8363620
M3 - Conference contribution
T3 - Proceedings - International Symposium on Biomedical Imaging
SP - 479
EP - 483
BT - 2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018
PB - IEEE Computer Society
T2 - 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018
Y2 - 4 April 2018 through 7 April 2018
ER -