Multi-agent reinforcement learning pipeline for anatomical landmark detection in minipigs

Michal Brzus; Alexander B. Powers; Kevin S. Knoernschild; Jessica C. Sieren; Hans J. Johnson

doi:10.1117/12.2611008

4 April 2022 Multi-agent reinforcement learning pipeline for anatomical landmark detection in minipigs

Michal Brzus, Alexander B. Powers, Kevin S. Knoernschild, Jessica C. Sieren, Hans J. Johnson

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 1203229 (2022) https://doi.org/10.1117/12.2611008
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

In recent years, the use of large animals in neurological research has escalated due to advantages over small animals. Unfortunately, large animal imaging researchers lack functional automated medical imaging tools, requiring laborious manual processing. As a response, we have implemented a Reinforcement Learning pipeline for brain anatomical landmark detection in minipig MRIs. Leveraging a deep convolutional network, two-step detection process, and multiple Deep-Q multi-agent networks, our approach is suitable for accurate landmark detection in large animals. Using a heterogeneous dataset containing 154 minipig images, we achieved an average accuracy of 1.56mm on predicting 19 landmarks.

Conference Presentation

Citation Download Citation

Michal Brzus, Alexander B. Powers, Kevin S. Knoernschild, Jessica C. Sieren, and Hans J. Johnson "Multi-agent reinforcement learning pipeline for anatomical landmark detection in minipigs", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 1203229 (4 April 2022); https://doi.org/10.1117/12.2611008

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