Conference proceeding
Robust Automatic Multiple Landmark Detection
2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1178-1182
IEEE International Symposium on Biomedical Imaging (ISBI), 17th (2020)
04/2020
DOI: 10.1109/ISBI45749.2020.9098329
Abstract
Reinforcement learning (RL) has proven to be a powerful tool for automatic single landmark detection in 3D medical images. In this work, we extend RL-based single landmark detection to detect multiple landmarks simultaneously in the presence of missing data in the form of defaced 3D head MR images. Our purposed technique is both time-efficient and robust to missing data. We demonstrate that adding auxiliary landmarks can improve the accuracy and robustness of estimating primary target landmark locations. The multi-agent deep Q-network (DQN) approach described here detects landmarks within 2mm, even in the presence of missing data.
Details
- Title: Subtitle
- Robust Automatic Multiple Landmark Detection
- Creators
- Arjit Jain - University of IowaAlexander Powers - University of IowaHans J Johnson - University of Iowa
- Resource Type
- Conference proceeding
- Publication Details
- 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), Vol.2020-, pp.1178-1182
- Conference
- IEEE International Symposium on Biomedical Imaging (ISBI), 17th (2020)
- Publisher
- IEEE
- DOI
- 10.1109/ISBI45749.2020.9098329
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Language
- English
- Date published
- 04/2020
- Academic Unit
- Electrical and Computer Engineering; Roy J. Carver Department of Biomedical Engineering; Psychiatry
- Record Identifier
- 9984185463502771
Metrics
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