A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation
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 sele...
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Elsevier
2025-09-01
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| Series: | Osteoarthritis and Cartilage Open |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2665913125000810 |
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| author | Wolfgang Wirth Felix Eckstein |
| author_facet | Wolfgang Wirth Felix Eckstein |
| author_sort | Wolfgang Wirth |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ecd1260c6b364cb0a8a62ba0b883a983 |
| institution | DOAJ |
| issn | 2665-9131 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Osteoarthritis and Cartilage Open |
| spelling | doaj-art-ecd1260c6b364cb0a8a62ba0b883a9832025-08-20T02:39:09ZengElsevierOsteoarthritis and Cartilage Open2665-91312025-09-017310064510.1016/j.ocarto.2025.100645A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validationWolfgang Wirth0Felix Eckstein1Chondrometrics GmbH, Freilassing, Germany; Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology, Paracelsus Medical University, Salzburg, Austria; Ludwig-Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, Austria; Corresponding author. Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology, Paracelsus Medical University, Strubergasse 21, 5020 Salzburg, Austria.Chondrometrics GmbH, Freilassing, Germany; Research Program for Musculoskeletal Imaging, Center for Anatomy and Cell Biology, Paracelsus Medical University, Salzburg, Austria; Ludwig-Boltzmann Institute for Arthritis and Rehabilitation, Paracelsus Medical University, Salzburg, AustriaObjective: 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.http://www.sciencedirect.com/science/article/pii/S2665913125000810Convolutional neural networkFully-automated analysisOsteoarthritisSevere radiographic OAImaging |
| spellingShingle | Wolfgang Wirth Felix Eckstein A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation Osteoarthritis and Cartilage Open Convolutional neural network Fully-automated analysis Osteoarthritis Severe radiographic OA Imaging |
| title | A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation |
| title_full | A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation |
| title_fullStr | A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation |
| title_full_unstemmed | A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation |
| title_short | A fully-automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis – Method development and validation |
| title_sort | fully automated technique for cartilage morphometry in knees with severe radiographic osteoarthritis method development and validation |
| topic | Convolutional neural network Fully-automated analysis Osteoarthritis Severe radiographic OA Imaging |
| url | http://www.sciencedirect.com/science/article/pii/S2665913125000810 |
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