University of Oulu

E. Panfilov, A. Tiulpin, S. Klein, M. T. Nieminen and S. Saarakkala, "Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation," 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), Seoul, Korea (South), 2019, pp. 450-459,

Improving robustness of deep learning based knee MRI segmentation : mixup and adversarial domain adaptation

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Author: Panfilov, Egor1; Tiulpin, Aleksei1,2; Klein, Stefan3;
Organizations: 1University of Oulu, Oulu, Finland
2Oulu University Hospital, Oulu, Finland
3Erasmus MC, Rotterdam, The Netherlands
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 1.8 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2019
Publish Date: 2020-06-03


Degeneration of articular cartilage (AC) is actively studied in knee osteoarthritis (OA) research via magnetic resonance imaging (MRI). Segmentation of AC tissues from MRI data is an essential step in quantification of their damage. Deep learning (DL) based methods have shown potential in this realm and are the current state-of-the-art, however, their robustness to heterogeneity of MRI acquisition settings remains an open problem. In this study, we investigated two modern regularization techniques — mixup and adversarial unsupervised domain adaptation (UDA) — to improve the robustness of DL-based knee cartilage segmentation to new MRI acquisition settings. Our validation setup included two datasets produced by different MRI scanners and using distinct data acquisition protocols. We assessed the robustness of automatic segmentation by comparing mixup and UDA approaches to a strong baseline method at different OA severity stages and, additionally, in relation to anatomical locations. Our results showed that for moderate changes in knee MRI data acquisition settings both approaches may provide notable improvements in the robustness, which are consistent for all stages of the disease and affect the clinically important areas of the knee joint. However, mixup may be considered as a recommended approach, since it is more computationally efficient and does not require additional data from the target acquisition setup.

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Series: IEEE International Conference on Computer Vision workshops
ISSN: 2473-9944
ISSN-E: 2473-9936
ISSN-L: 2473-9944
ISBN: 978-1-7281-5023-9
ISBN Print: 978-1-7281-5024-6
Pages: 450 - 459
DOI: 10.1109/ICCVW.2019.00057
Host publication: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW) : 27th October- 2nd November 2019, Seoul, Korea
Conference: IEEE/CVF International Conference on Computer Vision Workshop
Type of Publication: A4 Article in conference proceedings
Field of Science: 217 Medical engineering
Funding: The OAI is a public-private partnership comprised of five contracts (N01-AR-2-2258; N01-AR-2-2259; N01-AR-2-2260; N01-AR-2-2261; N01-AR-2-2262) funded by the National Institutes of Health, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Merck Research Laboratories; Novartis Pharmaceuticals Corporation, GlaxoSmithKline; and Pfizer, Inc. Private sector funding for the OAI is managed by the Foundation for the National Institutes of Health. The authors would like to acknowledge the following funding sources: strategic funding of University of Oulu (Infotech Oulu), Sigrid Juselius foundation, and KAUTE foundation, Finland.
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