TY - GEN
T1 - Automatic Fetal Brain Extraction Using Multi-stage U-Net with Deep Supervision
AU - Lou, Jingjiao
AU - Li, Dengwang
AU - Bui, Toan Duc
AU - Zhao, Fenqiang
AU - Sun, Liang
AU - Li, Gang
AU - Shen, Dinggang
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Fetal brain extraction is one of the most essential steps for prenatal brain MRI reconstruction and analysis. However, due to the fetal movement within the womb, it is a challenging task to extract fetal brains from sparsely-acquired imaging stacks typically with motion artifacts. To address this problem, we propose an automatic brain extraction method for fetal magnetic resonance imaging (MRI) using multi-stage 2D U-Net with deep supervision (DS U-net). Specifically, we initially employ a coarse segmentation derived from DS U-net to define a 3D bounding box for localizing the position of the brain. The DS U-net is trained with deep supervision loss to acquire more powerful discrimination capability. Then, another DS U-net focuses on the extracted region to produce finer segmentation. The final segmentation results are obtained by performing refined segmentation. We validate the proposed method on 80 stacks of training images and 43 testing stacks. The experimental results demonstrate the precision and robustness of our method with the average Dice coefficient of 91.69%, outperforming the existing methods.
AB - Fetal brain extraction is one of the most essential steps for prenatal brain MRI reconstruction and analysis. However, due to the fetal movement within the womb, it is a challenging task to extract fetal brains from sparsely-acquired imaging stacks typically with motion artifacts. To address this problem, we propose an automatic brain extraction method for fetal magnetic resonance imaging (MRI) using multi-stage 2D U-Net with deep supervision (DS U-net). Specifically, we initially employ a coarse segmentation derived from DS U-net to define a 3D bounding box for localizing the position of the brain. The DS U-net is trained with deep supervision loss to acquire more powerful discrimination capability. Then, another DS U-net focuses on the extracted region to produce finer segmentation. The final segmentation results are obtained by performing refined segmentation. We validate the proposed method on 80 stacks of training images and 43 testing stacks. The experimental results demonstrate the precision and robustness of our method with the average Dice coefficient of 91.69%, outperforming the existing methods.
KW - Brain extraction
KW - Convolutional neural network
KW - Fetal MRI
UR - http://www.scopus.com/inward/record.url?scp=85075686677&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075686677&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32692-0_68
DO - 10.1007/978-3-030-32692-0_68
M3 - Conference contribution
AN - SCOPUS:85075686677
SN - 9783030326913
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 592
EP - 600
BT - Machine Learning in Medical Imaging - 10th International Workshop, MLMI 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Suk, Heung-Il
A2 - Liu, Mingxia
A2 - Lian, Chunfeng
A2 - Yan, Pingkun
PB - Springer
T2 - 10th International Workshop on Machine Learning in Medical Imaging, MLMI 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019
Y2 - 13 October 2019 through 13 October 2019
ER -