Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation.

Journal: Computers in biology and medicine
Published Date: Dec 16, 2023

Abstract

Medical image segmentation is a fundamental and critical step in many image-guided clinical approaches. Recent success of deep learning-based segmentation methods usually relies on a large amount of labeled data, which is particularly difficult and costly to obtain, especially in the medical imaging domain where only experts can provide reliable and accurate annotations. Semi-supervised learning has emerged as an appealing strategy and been widely applied to medical image segmentation tasks to train deep models with limited annotations. In this paper, we present a comprehensive review of recently proposed semi-supervised learning methods for medical image segmentation and summarize both the technical novelties and empirical results. Furthermore, we analyze and discuss the limitations and several unsolved problems of existing approaches. We hope this review can inspire the research community to explore solutions to this challenge and further advance the field of medical image segmentation.

Authors

  • Rushi Jiao
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; School of Engineering Medicine, Beihang University, Beijing, 100191, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address: rushijiao@sjtu.edu.cn.
  • Yichi Zhang
    Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
  • Le Ding
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China.
  • Bingsen Xue
    School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China; Shanghai Artificial Intelligence Laboratory, Shanghai, 200232, China. Electronic address: bingsenxue@sjtu.edu.cn.
  • Jicong Zhang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China; Heifei Innovation Research Institute, Beihang University, Hefei, China; Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China; Beijing Advanced Innovation Centre for Big Data-Based Precision Medicine, Beihang University, Beijing, China; School of Biomedical Engineering, Anhui Medical University, Heifei, China. Electronic address: jicongzhang@buaa.edu.cn.
  • Rong Cai
    School of Engineering Medicine, Beihang University, Beijing, 100191, China; Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beihang University, Beijing, 100191, China. Electronic address: cairong@buaa.edu.cn.
  • Cheng Jin
    Department of Pathology, Hangzhou Women's Hospital, Hangzhou, 310008, Zhejiang, China.

Keywords

External Resources

Popular Topics
Recent Journals