Cross-Cohort Automatic Knee MRI Segmentation With Multi-Planar U-Nets

Mathias Perslev*, Akshay Pai, Jos Runhaar, Christian Igel, Erik B. Dam

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)
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Abstract

Background: Segmentation of medical image volumes is a time-consuming manual task. Automatic tools are often tailored toward specific patient cohorts, and it is unclear how they behave in other clinical settings. Purpose: To evaluate the performance of the open-source Multi-Planar U-Net (MPUnet), the validated Knee Imaging Quantification (KIQ) framework, and a state-of-the-art two-dimensional (2D) U-Net architecture on three clinical cohorts without extensive adaptation of the algorithms. Study Type: Retrospective cohort study. Subjects: A total of 253 subjects (146 females, 107 males, ages 57 ± 12 years) from three knee osteoarthritis (OA) studies (Center for Clinical and Basic Research [CCBR], Osteoarthritis Initiative [OAI], and Prevention of OA in Overweight Females [PROOF]) with varying demographics and OA severity (64/37/24/53/2 scans of Kellgren and Lawrence [KL] grades 0–4). Field Strength/Sequence: 0.18 T, 1.0 T/1.5 T, and 3 T sagittal three-dimensional fast-spin echo T1w and dual-echo steady-state sequences. Assessment: All models were fit without tuning to knee magnetic resonance imaging (MRI) scans with manual segmentations from three clinical cohorts. All models were evaluated across KL grades. Statistical Tests: Segmentation performance differences as measured by Dice coefficients were tested with paired, two-sided Wilcoxon signed-rank statistics with significance threshold α = 0.05. Results: The MPUnet performed superior or equal to KIQ and 2D U-Net on all compartments across three cohorts. Mean Dice overlap was significantly higher for MPUnet compared to KIQ and U-Net on CCBR ((Formula presented.) vs. (Formula presented.) and (Formula presented.)), significantly higher than KIQ and U-Net OAI ((Formula presented.) vs. (Formula presented.) and (Formula presented.), and not significantly different from KIQ while significantly higher than 2D U-Net on PROOF ((Formula presented.) vs. (Formula presented.), (Formula presented.), and (Formula presented.). The MPUnet performed significantly better on (Formula presented.) KL grade 3 CCBR scans with (Formula presented.) vs. (Formula presented.) for KIQ and (Formula presented.) for 2D U-Net. Data Conclusion: The MPUnet matched or exceeded the performance of state-of-the-art knee MRI segmentation models across cohorts of variable sequences and patient demographics. The MPUnet required no manual tuning making it both accurate and easy-to-use. Level of Evidence: 3. Technical Efficacy: Stage 2.

Original languageEnglish
Pages (from-to)1650-1663
Number of pages14
JournalJournal of Magnetic Resonance Imaging
Volume55
Issue number6
DOIs
Publication statusE-pub ahead of print - 17 Dec 2021

Bibliographical note

Funding Information:
Mathias Perslev and Christian Igel gratefully acknowledge support from the Independent Research Fund Denmark through the project “U‐Sleep” (project number 9131‐00099B). Akshay Pai and Christian Igel gratefully acknowledge support from The Danish Industry Foundation as part of the initiative AI Denmark. The OAI collection was provided by the OAI. 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, and 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. This manuscript was prepared using an OAI public use dataset and does not necessarily reflect the opinions or views of the OAI investigators, the NIH, or the private funding partners.

Publisher Copyright:
© 2021 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.

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