TY - GEN
T1 - Joint Image Quality Assessment and Brain Extraction of Fetal MRI Using Deep Learning
AU - Liao, Lufan
AU - Zhang, Xin
AU - Zhao, Fenqiang
AU - Zhong, Tao
AU - Pei, Yuchen
AU - Xu, Xiangmin
AU - Wang, Li
AU - Zhang, He
AU - Shen, Dinggang
AU - Li, Gang
N1 - Funding Information:
Acknowledgements. XZ and XX are supported in part by the NSFC under grant U1801262, Guangzhou Key Laboratory of Body Data Science under grant 201605030011.
PY - 2020
Y1 - 2020
N2 - Quality assessment (QA) and brain extraction (BE) are two fundamental steps in 3D fetal brain MRI reconstruction and quantification. Conventionally, QA and BE are performed independently, ignoring the inherent relation of the two closely-related tasks. However, both of them focus on the brain region representation, so they can be jointly optimized to ensure the network to learn shared features and avoid overfitting. To this end, we propose a novel multi-stage deep learning model for joint QA and BE of fetal MRI. The locations and orientations of fetal brains are randomly variable, and the shapes and appearances of fetal brains change remarkably across gestational ages, thus imposing great challenges to extract shared features of QA and BE. To address these problems, we firstly design a brain detector to locate the brain region. Then we introduce the deformable convolution to adaptively adjust the receptive field for dealing with variable brain shapes. Finally, a task-specific module is used for image QA and BE simultaneously. To obtain a well-trained model, we further propose a multi-step training strategy. We cross validate our method on two independent fetal MRI datasets acquired from different scanners with different imaging protocols, and achieve promising performance.
AB - Quality assessment (QA) and brain extraction (BE) are two fundamental steps in 3D fetal brain MRI reconstruction and quantification. Conventionally, QA and BE are performed independently, ignoring the inherent relation of the two closely-related tasks. However, both of them focus on the brain region representation, so they can be jointly optimized to ensure the network to learn shared features and avoid overfitting. To this end, we propose a novel multi-stage deep learning model for joint QA and BE of fetal MRI. The locations and orientations of fetal brains are randomly variable, and the shapes and appearances of fetal brains change remarkably across gestational ages, thus imposing great challenges to extract shared features of QA and BE. To address these problems, we firstly design a brain detector to locate the brain region. Then we introduce the deformable convolution to adaptively adjust the receptive field for dealing with variable brain shapes. Finally, a task-specific module is used for image QA and BE simultaneously. To obtain a well-trained model, we further propose a multi-step training strategy. We cross validate our method on two independent fetal MRI datasets acquired from different scanners with different imaging protocols, and achieve promising performance.
KW - Brain extraction
KW - Fetal MRI
KW - Quality assessment
UR - http://www.scopus.com/inward/record.url?scp=85092789062&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85092789062&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-59725-2_40
DO - 10.1007/978-3-030-59725-2_40
M3 - Conference contribution
AN - SCOPUS:85092789062
SN - 9783030597245
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 415
EP - 424
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 - 23rd International Conference, Proceedings
A2 - Martel, Anne L.
A2 - Abolmaesumi, Purang
A2 - Stoyanov, Danail
A2 - Mateus, Diana
A2 - Zuluaga, Maria A.
A2 - Zhou, S. Kevin
A2 - Racoceanu, Daniel
A2 - Joskowicz, Leo
PB - Springer Science and Business Media Deutschland GmbH
T2 - 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2020
Y2 - 4 October 2020 through 8 October 2020
ER -