FISMI-DRL: A Framework for Interactive Segmentation of Medical Image Based On Deep Reinforcement Learning
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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Reinforcement Learning Approach to Instance Segmentation Using Double DQN
Denknalbant, A., Ahangari, M., Kaya, B., Saidburkhan, A., & Arıcan, E. (2025) · Innovations in Intelligent Systems and Applications Conference (ASYU) · doi:10.1109/asyu67174.2025.11208352
“In a similar direction, deep reinforcement learning and attention mechanism architectures have been proposed for high accuracy and reduced user interventions in biomedical and aerial imagery [13], [14].”
- 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
- Semantic Scholar
- f48bdadeb0e775aff50292713d1ac59858ba8f05
- 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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