In this section
@inproceedings{Sinclair:2017:10.1007/978-3-319-66185-8_38,
author = {Sinclair, M and Bai, W and Puyol-Antón, E and Oktay, O and Rueckert, D and King, AP},
doi = {10.1007/978-3-319-66185-8_38},
pages = {332--340},
title = {Fully automated segmentation-based respiratory motion correction of multiplanar cardiac magnetic resonance images for large-scale datasets},
url = {http://dx.doi.org/10.1007/978-3-319-66185-8_38},
year = {2017}
}
TY - CPAPER
AB - © Springer International Publishing AG 2017. Cardiac magnetic resonance (CMR) can be used for quantitative analysis of heart function. However, CMR imaging typically involves acquiring 2D image planes during separate breath-holds, often resulting in misalignment of the heart between image planes in 3D. Accurate quantitative analysis requires a robust 3D reconstruction of the heart from CMR images, which is adversely affected by such motion artifacts. Therefore, we propose a fully automated method for motion correction of CMR planes using segmentations produced by fully convolutional neural networks (FCNs). FCNs are trained on 100 UK Biobank subjects to produce short-axis and long-axis segmentations, which are subsequently used in an iterative registration algorithm for correcting breath-hold induced motion artifacts. We demonstrate significant improvements in motion-correction over image-based registration, with strong correspondence to results obtained using manual segmentations. We also deploy our automatic method on 9,353 subjects in the UK Biobank database, demonstrating significant improvements in 3D plane alignment.
AU - Sinclair,M
AU - Bai,W
AU - Puyol-Antón,E
AU - Oktay,O
AU - Rueckert,D
AU - King,AP
DO - 10.1007/978-3-319-66185-8_38
EP - 340
PY - 2017///
SN - 0302-9743
SP - 332
TI - Fully automated segmentation-based respiratory motion correction of multiplanar cardiac magnetic resonance images for large-scale datasets
UR - http://dx.doi.org/10.1007/978-3-319-66185-8_38
ER -
