Cited by 1Medical Image Segmentation TechniquesAdvanced Neural Network ApplicationsMedical Imaging and Analysis

FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning

Qian, Chen, Yang, Hao, Li, Jiyun·2023·2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)

Abstract

At present, deep learning-based medical image segmentation algorithms have achieved fast and accurate semantic segmentation. However, their segmentation accuracy is still challenging to reach the clinical use standard, requiring further refinement by medical experts. Therefore, some researchers have turned their attention to interactive segmentation methods, which introduce human interaction to obtain information gain. Such methods model the dynamics of the image annotation process state and can effectively improve the segmentation accuracy under the interaction of medical experts. In this paper, we put forward a novel framework for the interactive segmentation of medical images based on deep reinforcement learning, namely FISMI-DRL. The experimental results demonstrate that our model achieves high segmentation accuracy and interaction efficiency.

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Crossref · 10.1109/smc53992.2023.10393973

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Authors
Qian, C., Yang, H., & Li, J.
Affiliations
  • School of Computer Sci. & Tech., Donghua University,Shanghai,China
  • Donghua University (CN)
Venue
2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)
Publisher
IEEE
Published
2023-10-01
Pages
2106-2111
ISSN
1062-922X (ISSN-L)
OpenAlex
W4391331271
Cited by
1
References
28
Citations by year
2025: 1
FWCI
0.11
Type
conference paper
Language
en
Open access
closed
Locations
Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Biomedical Engineering › Medical Imaging and Analysis
Keywords
interactive segmentation, medical image segmentation, deep reinforcement learning, human-in-the-loop annotation, interaction efficiency

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