一种用于严重放射性骨关节炎膝关节软骨形态测量的全自动技术-方法开发和验证

IF 2.8

Osteoarthritis and cartilage open Pub Date : 2025-07-04 DOI:10.1016/j.ocarto.2025.100645

Wolfgang Wirth , Felix Eckstein

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引用次数: 0

摘要

目的利用卷积神经网络（cnn）对严重影像学骨关节炎患者膝关节关节软骨和软骨下骨的全自动分割提出了挑战。为了克服这一问题，我们提出了一种基于选择的多图谱配准的自动后处理方法来重建软骨下骨（tAB）的总面积。我们评估的一致性，准确性和纵向敏感性的软骨变化的这种新方法。基于cnn的模型通过人工软骨分割进行训练，这些软骨分割来自膝关节矢状面DESS和冠状面FLASH MRI，并伴有x线摄影（KLG2-4）或严重的x线摄影骨关节炎（仅KLG4）。然后将这些应用于手动软骨分割的KLG4测试膝关节。自动后处理应用于重建标签缺失的部分，并细化分割，特别是对于数据库。采用Dice相似系数（DSC）和Bland-Altman分析评估自动软骨分析的一致性和准确性；采用标准化反应均值（SRM）评估对一年变化的敏感性。结果与KLG4相比，在KLG2-4上训练的cnn具有更强的一致性（DSC为0.80±0.07 ~ 0.89±0.05），软骨厚度（1.2% ~ 8.4%）和tAB面积（- 0.4% ~ 4.3%）的系统偏移量更小；总的来说，结果优于那些没有基于注册的后处理。人工分割DESS对变化的敏感性最高(SRM≥- 0.69；automated:≥−0.56)和自动分割FLASH(≥−0.74；手动≥−0.44)。结论基于cnn的分割结合基于配准的后处理可准确描绘tABs/dABs，大大改善了严重影像学骨关节炎膝关节软骨和软骨下骨形态的全自动（纵向）分析。

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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 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.

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来源期刊

Osteoarthritis and cartilage open Orthopedics, Sports Medicine and Rehabilitation

CiteScore

3.30

自引率

0.00%

发文量