Mr. Samuel Robertson Profile

Samuel Robertson

at Univ of Calgary

SPIE Involvement:

Author

Publications (1)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

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

Read Abstract +

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.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years