Cross‐Cohort Automatic Knee MRI Segmentation With Multi‐Planar U‐Nets. Issue 6 (17th December 2021)

• Record Type: Journal Article
• Authors: Perslev, Mathias; Pai, Akshay; Runhaar, Jos; Igel, Christian; Dam, Erik B.
• Is Part Of: Journal of magnetic resonance imaging. Volume 55:Issue 6(2022)
• Page Start: 1650
• Page End: 1663
• Publication Date: 2021-12-17
• Subjects: knee segmentation -- deep learning -- magnetic resonance imaging -- open‐source software; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27978
• Languages: English
• ISSNs: 1053-1807

Abstract

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 ( 0.83 ± 0.04 vs. 0.81 ± 0.06 and 0.82 ± 0.05 ), significantly higher than KIQ and U‐Net OAI ( 0.86 ± 0.03 vs. 0.84 ± 0.04 and 0.85 ± 0.03 ), and not significantly different from KIQ while significantly higher than 2D U‐Net on PROOF ( 0.78 ± 0.07 vs. 0.77 ± 0.07, P = 0.10, and 0.73 ± 0.07 ) . The MPUnet performed significantly better on N = 22 KL grade 3 CCBR scans with 0.78 ± 0.06 vs. 0.75 ± 0.08 for KIQ and 0.76 ± 0.06 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