TY - GEN
T1 - A Bootstrap Self-training Method for Sequence Transfer
T2 - 3rd International Workshop on Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, UNSURE 2021, and the 6th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2021, held in conjunction with the 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
AU - Specktor-Fadida, Bella
AU - Link-Sourani, Daphna
AU - Ferster-Kveller, Shai
AU - Ben-Sira, Liat
AU - Miller, Elka
AU - Ben-Bashat, Dafna
AU - Joskowicz, Leo
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - Quantitative volumetric evaluation of the placenta in fetal MRI scans is an important component of the fetal health evaluation. However, manual segmentation of the placenta is a time-consuming task that requires expertise and suffers from high observer variability. Deep learning methods for automatic segmentation are effective but require manually annotated datasets for each scanning sequence. We present a new method for bootstrapping automatic placenta segmentation by deep learning on different MRI sequences. The method consists of automatic placenta segmentation with two networks trained on labeled cases of one sequence followed by automatic adaptation using self-training of the same network to a new sequence with new unlabeled cases of this sequence. It uses a novel combined contour and soft Dice loss function for both the placenta ROI detection and segmentation networks. Our experimental studies for the FIESTA sequence yields a Dice score of 0.847 on 21 test cases with only 16 cases in the training set. Transfer to the TRUFI sequence yields a Dice score of 0.78 on 15 test cases, a significant improvement over the network results without transfer learning. The contour Dice loss and self-training approach achieve state-of-the art placenta segmentation results by sequence transfer bootstrapping.
AB - Quantitative volumetric evaluation of the placenta in fetal MRI scans is an important component of the fetal health evaluation. However, manual segmentation of the placenta is a time-consuming task that requires expertise and suffers from high observer variability. Deep learning methods for automatic segmentation are effective but require manually annotated datasets for each scanning sequence. We present a new method for bootstrapping automatic placenta segmentation by deep learning on different MRI sequences. The method consists of automatic placenta segmentation with two networks trained on labeled cases of one sequence followed by automatic adaptation using self-training of the same network to a new sequence with new unlabeled cases of this sequence. It uses a novel combined contour and soft Dice loss function for both the placenta ROI detection and segmentation networks. Our experimental studies for the FIESTA sequence yields a Dice score of 0.847 on 21 test cases with only 16 cases in the training set. Transfer to the TRUFI sequence yields a Dice score of 0.78 on 15 test cases, a significant improvement over the network results without transfer learning. The contour Dice loss and self-training approach achieve state-of-the art placenta segmentation results by sequence transfer bootstrapping.
KW - Deep learning segmentation
KW - Unsupervised domain adaptation
KW - fetal MRI
UR - http://www.scopus.com/inward/record.url?scp=85117142926&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87735-4_18
DO - 10.1007/978-3-030-87735-4_18
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AN - SCOPUS:85117142926
SN - 9783030877347
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 189
EP - 199
BT - Uncertainty for Safe Utilization of Machine Learning in Medical Imaging, and Perinatal Imaging, Placental and Preterm Image Analysis - 3rd International Workshop, UNSURE 2021, and 6th International Workshop, PIPPI 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Sudre, Carole H.
A2 - Licandro, Roxane
A2 - Baumgartner, Christian
A2 - Melbourne, Andrew
A2 - Dalca, Adrian
A2 - Hutter, Jana
A2 - Tanno, Ryutaro
A2 - Abaci Turk, Esra
A2 - Van Leemput, Koen
A2 - Torrents Barrena, Jordina
A2 - Wells, William M.
A2 - Macgowan, Christopher
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 1 October 2021 through 1 October 2021
ER -
