A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI

Bella Specktor-Fadida, Daphna Link-Sourani, Shai Ferster-Kveller, Liat Ben-Sira, Elka Miller, Dafna Ben-Bashat, Leo Joskowicz

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

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.

Original languageEnglish
Title of host publicationUncertainty 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
EditorsCarole H. Sudre, Roxane Licandro, Christian Baumgartner, Andrew Melbourne, Adrian Dalca, Jana Hutter, Ryutaro Tanno, Esra Abaci Turk, Koen Van Leemput, Jordina Torrents Barrena, William M. Wells, Christopher Macgowan
PublisherSpringer Science and Business Media Deutschland GmbH
Pages189-199
Number of pages11
ISBN (Print)9783030877347
DOIs
StatePublished - 2021
Externally publishedYes
Event3rd 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 - Virtual, Online
Duration: 1 Oct 20211 Oct 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12959 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd 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
CityVirtual, Online
Period1/10/211/10/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Deep learning segmentation
  • fetal MRI
  • Unsupervised domain adaptation

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

Fingerprint

Dive into the research topics of 'A Bootstrap Self-training Method for Sequence Transfer: State-of-the-Art Placenta Segmentation in fetal MRI'. Together they form a unique fingerprint.

Cite this