TY - GEN
T1 - Deep Learning for Cardiac Motion Estimation
T2 - 10th International Workshop on Statistical Atlases and Computational Models of the Heart, STACOM 2019, held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
AU - Qiu, Huaqi
AU - Qin, Chen
AU - Le Folgoc, Loic
AU - Hou, Benjamin
AU - Schlemper, Jo
AU - Rueckert, Daniel
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2020.
PY - 2020
Y1 - 2020
N2 - Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.
AB - Deep learning based registration methods have emerged as alternatives to traditional registration methods, with competitive accuracy and significantly less runtime. Two different strategies have been proposed to train such deep learning registration networks: supervised training strategy where the model is trained to regress to generated ground truth deformation; and unsupervised training strategy where the model directly optimises the similarity between the registered images. In this work, we directly compare the performance of these two training strategies for cardiac motion estimation on cardiac cine MR sequences. Testing on real cardiac MRI data shows that while the supervised training yields more regular deformation, the unsupervised more accurately captures the deformation of anatomical structures in cardiac motion.
UR - http://www.scopus.com/inward/record.url?scp=85081923511&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-39074-7_20
DO - 10.1007/978-3-030-39074-7_20
M3 - Conference contribution
AN - SCOPUS:85081923511
SN - 9783030390730
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 186
EP - 194
BT - Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges - 10th International Workshop, STACOM 2019, Held in Conjunction with MICCAI 2019, Revised Selected Papers
A2 - Pop, Mihaela
A2 - Sermesant, Maxime
A2 - Camara, Oscar
A2 - Zhuang, Xiahai
A2 - Li, Shuo
A2 - Young, Alistair
A2 - Mansi, Tommaso
A2 - Suinesiaputra, Avan
PB - Springer
Y2 - 13 October 2019 through 13 October 2019
ER -