Forschung & Innovation
Publikationen
A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis - Method development and validation
PMU Autor*innen
Wolfgang Wirth, Felix Eckstein
Alle Autor*innen
Wolfgang Wirth, Felix Eckstein
Fachzeitschrift
Osteoarthritis and Cartilage Open
Kurzfassung
Objective: Denuded areas of subchondral bone (dAB) pose a challenge for fully automated segmentation of articular cartilage and subchondral bone in knees with severe radiographic osteoarthritis using convolutional neural networks (CNNs). Here we propose an automated post-processing relying on a selection-based multi-atlas registration for reconstructing the total area of subchondral bone (tAB) to overcome this issue. We evaluate the agreement, accuracy and longitudinal sensitivity to cartilage change of this novel methodology. Design: CNN-based models were trained using manual cartilage segmentations from sagittal DESS and coronal FLASH MRI of knees with radiographic (KLG2-4) or severe radiographic osteoarthritis (KLG4 only). These were then applied to KLG4 test knees with manual cartilage segmentations. Automated post-processing was applied to reconstruct missing parts of the tAB and to refine the segmentations, particularly for dABs. The agreement and accuracy of automated cartilage analysis were evaluated using Dice Similarity Coefficients (DSC) and Bland-Altman analyses; sensitivity to one-year change was assessed using the standardized response mean (SRM). Results: Stronger agreement (DSC 0.80 +/- 0.07 to 0.89 +/- 0.05) and lower systematic offsets for cartilage thickness (1.2 %-8.4 %) and tAB area (-0.4 %-4.3 %) were observed for CNNs trained on KLG2-4 rather than KLG4 knees; overall, results were superior to those without registration-based post-processing. Sensitivity to change was greatest for manual segmentation of DESS (SRM >= -0.69; automated: >=-0.56) and for automated segmentation of FLASH (>=-0.74; manual >=-0.44). Conclusion: CNN-based segmentation combined with registration-based post-processing for accurate delineation of tABs/dABs substantially improves fully-automated (longitudinal) analysis of cartilage and subchondral bone morphology in knees with severe radiographic osteoarthritis.
Keywords
OSTEOARTHRITIS, Convolutional neural network, Fully-automated analysis, Imaging, Severe radiographic OA