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Impact Factor:7.7

DOI number:10.1016/j.compbiomed.2023.107840

Journal:Computers in Biology and Medicine

Funded by:国家自然科学基金

Key Words:Medical Image Segmentation，Semi-Supervised Learning，Convolutional Neural Network，Survey

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.

Indexed by:Journal paper

Document Code:MEDLINE:38157773

First-Level Discipline:Biomedical Engineering

Document Type:J

Volume:169

Page Number:107840

ISSN No.:1879-0534

Translation or Not:no

Date of Publication:2024-02-01

Included Journals:SCI、SSCI