Soft-Label Guided Semi-Supervised Learning for Bi-Ventricle Segmentation in Cardiac Cine MRI

Qi Chang, Zhennan Yan, Yixuan Lou, Leon Axel, Dimitris N. Metaxas

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Deep convolutional neural networks have been applied to medical image segmentation tasks successfully in recent years by taking advantage of a large amount of training data with golden standard annotations. However, it is difficult and expensive to obtain good-quality annotations in practice. This work aims to propose a novel semi-supervised learning framework to improve the ventricle segmentation from 2D cine MR images. Our method is efficient and effective by computing soft labels dynamically for the unlabeled data. Specifically, we obtain the soft labels, rather than hard labels, from a teacher model in every learning iteration. The uncertainty of the target label of unlabeled data is intrinsically encoded in the soft label. The soft label can be improved towards the ideal target in training. We use a separate loss to regularize the unlabeled data to produce similar probability distribution as the soft labels in each iteration. Experiments show that our method outperforms a state-of-the-art semi-supervised method.

Original languageEnglish (US)
Title of host publicationISBI 2020 - 2020 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages1752-1755
Number of pages4
ISBN (Electronic)9781538693308
DOIs
StatePublished - Apr 2020
Event17th IEEE International Symposium on Biomedical Imaging, ISBI 2020 - Iowa City, United States
Duration: Apr 3 2020Apr 7 2020

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2020-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference17th IEEE International Symposium on Biomedical Imaging, ISBI 2020
Country/TerritoryUnited States
CityIowa City
Period4/3/204/7/20

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Keywords

  • Semi-supervised segmentation
  • soft-label

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