Mr. Samuel Robertson Profile

Samuel Robertson

at Univ of Calgary

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Proceedings Article | 4 April 2022 Presentation + Paper

Stroke lesion localization in 3D MRI datasets with deep reinforcement learning

Samuel Robertson, Anup Tuladhar, Deepthi Rajashekar, Nils Forkert

Proceedings Volume 12033, 120330M (2022) https://doi.org/10.1117/12.2613039

KEYWORDS: Magnetic resonance imaging, Medical imaging, Ischemic stroke, Image segmentation, Data modeling, Machine vision, Machine learning, Computer vision technology

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The efficacy of stroke treatments is highly time-sensitive, and any computer-aided diagnosis support method that can accelerate diagnosis and treatment initiation may improve patient outcomes. Within this context, lesion identification in MRI datasets can be time consuming and challenging, even for trained clinicians. Automatic lesion localization can expedite diagnosis by flagging datasets and corresponding regions of interest for further assessment. In this work, we propose a deep reinforcement learning agent to localize acute ischemic stroke lesions in MRI images. Therefore, we adapt novel techniques from the computer vision domain to medical image analysis, allowing the agent to sequentially localize multiple lesions in a single dataset. The proposed method was developed and evaluated using a database consisting of fluid attenuated inversion recovery (FLAIR) MRI datasets from 466 ischemic stroke patients acquired at multiple centers. 372 patients were used for training while 94 patients (20% of available data) were employed for testing. Furthermore, the model was tested using 58 datasets from an out-of-distribution test set to investigate the generalization error in more detail. The model achieved a Dice score of 0.45 on the hold-out test set and 0.43 on images from the out-of-distribution test set. In conclusion, we apply deep reinforcement learning to the clinically well-motivated task of localizing multiple ischemic stroke lesions in MRI images, and achieve promising results validated on a large and heterogeneous collection of datasets.

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