Dual-Teacher: Integrating Intra-domain and Inter-domain Teachers for Annotation-Efficient Cardiac Segmentation
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AbstractMedical image annotations are prohibitively time-consuming and expensive to obtain. To alleviate annotation scarcity, many approaches have been developed to efficiently utilize extra information, e.g., semi-supervised learning further exploring plentiful unlabeled data, domain adaptation including multi-modality learning and unsupervised domain adaptation resorting to the prior knowledge from additional modality. In this paper, we aim to investigate the feasibility of simultaneously leveraging abundant unlabeled data and well-established cross-modality data for annotation-efficient medical image segmentation. To this end, we propose a novel semi-supervised domain adaptation approach, namely Dual-Teacher, where the student model not only learns from labeled target data (e.g., CT), but also explores unlabeled target data and labeled source data (e.g., MR) by two teacher models. Specifically, the student model learns the knowledge of unlabeled target data from intra-domain teacher by encouraging prediction consistency, as well as the shape priors embedded in labeled source data from inter-domain teacher via knowledge distillation. Consequently, the student model can effectively exploit the information from all three data resources and comprehensively integrate them to achieve improved performance. We conduct extensive experiments on MM-WHS 2017 dataset and demonstrate that our approach is able to concurrently utilize unlabeled data and cross-modality data with superior performance, outperforming semi-supervised learning and domain adaptation methods with a large margin.
All Author(s) ListLi K., Wang S., Yu L., Heng P. A.
Name of ConferenceMICCAI 2020
Start Date of Conference04/10/2020
End Date of Conference08/10/2020
Place of ConferencePERU
Country/Region of ConferencePeru
Proceedings TitleLecture Notes in Computer Science
Year2020
Month10
Volume Number12261
PublisherSpringer
Pages418 - 427
ISBN978-3-030-59709-2
eISBN978-3-030-59710-8
LanguagesEnglish-United Kingdom
KeywordsSemi-supervised domain adaptation, Cross-modality segmentation, Cardiac segmentation

Last updated on 2021-10-04 at 23:41