Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets

Anshul Ratnaparkhi; Bilwaj Gaonkar; David Zarrin; Ien Li; Kirstin Cook; Azim Laiwalla; Bayard Wilson; Mark Attiah; Christine Ahn; Diane Villaroman; Bryan Yoo; Banafsheh Salehi; Joel Beckett; Luke Macyszyn

doi:10.1117/12.2611609

4 April 2022 Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets

Anshul Ratnaparkhi, Bilwaj Gaonkar, David Zarrin, Ien Li, Kirstin Cook, Azim Laiwalla, Bayard Wilson, Mark Attiah, Christine Ahn, Diane Villaroman, Bryan Yoo, Banafsheh Salehi, Joel Beckett, Luke Macyszyn

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120330I (2022) https://doi.org/10.1117/12.2611609
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Abstract

Machine learning algorithms tend to perform better within the setting wherein they are trained, a phenomenon known as the domain effect. Deep learning-based medical image segmentation algorithms are often trained using data acquired from specific scanners; however, these algorithms are expected to accurately segment anatomy in images acquired from scanners different from the ones used to obtain training images for such algorithms. In this work, we present evidence of a scanner and magnet strength specific domain effect for a deep-U-Net trained to segment spinal canals on axial MR images. The trained network performs better on new data from the same scanner and worse on data from other scanners, demonstrating a scanner-specific domain effect. We then construct ensembles of the U-Nets, in which each U-Net in the ensemble differs from others only in initialization. Finally, we demonstrate that these UNet ensembles reduce the differential between in-domain and out-of-domain performance, thereby mitigating the domain effect associated with single U-Nets. Our study evidences the importance of developing software robust to scanner-specific domain effects to handle scanner bias in Deep Learning.

Conference Presentation

Citation Download Citation

Anshul Ratnaparkhi, Bilwaj Gaonkar, David Zarrin, Ien Li, Kirstin Cook, Azim Laiwalla, Bayard Wilson, Mark Attiah, Christine Ahn, Diane Villaroman, Bryan Yoo, Banafsheh Salehi, Joel Beckett, and Luke Macyszyn "Ensembling mitigates scanner effects in deep learning medical image segmentation with deep-U-Nets", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120330I (4 April 2022); https://doi.org/10.1117/12.2611609

ACCESS THE FULL ARTICLE

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