College of Medicine, The Ohio State University, Columbus, OH 43210, USA.
Osteoarthritis Cartilage. 2010 Mar;18(3):344-53. doi: 10.1016/j.joca.2009.10.004. Epub 2009 Nov 5.
The goal of this study was to develop an algorithm to semi-automatically segment the meniscus in a series of magnetic resonance (MR) images to use for normal knees and those with moderate osteoarthritis (OA).
The segmentation method was developed then evaluated on 10 baseline MR images obtained from subjects with no evidence, symptoms, or risk factors of knee (OA), and 14 from subjects with established knee OA enrolled in the Osteoarthritis Initiative (OAI). After manually choosing a seed point within the meniscus, a threshold level was calculated through a Gaussian fit model. Under anatomical, intensity, and range constraints, a threshold operation was completed followed by conditional dilation and post-processing. The post-processing operation reevaluates the pixels included and excluded in the area surrounding the meniscus to improve accuracy. The developed method was evaluated for both normal and degenerative menisci by comparing the segmentation algorithm results with manual segmentations from five human readers.
The semi-automated segmentation method produces results similar to those of trained observers, with an average similarity index over 0.80 for normal participants and 0.75, 0.67, and 0.64 for participants with established knee OA with Osteoarthritis Research Society International (OARSI) joint space narrowing (JSN) scores of 0, one, and two respectively.
The semi-automatic segmentation method produced accurate and consistent segmentations of the meniscus when compared to manual segmentations in the assessment of normal menisci in mild to moderate OA. Future studies will examine the change in volume, thickness, and intensity characteristics at different stages of OA.
本研究旨在开发一种算法，以半自动分割一系列磁共振（MR）图像中的半月板，用于正常膝关节和中度骨关节炎（OA）的膝关节。
开发了分割方法，然后在 10 名基线 MR 图像上进行了评估，这些图像来自没有膝关节（OA）的证据、症状或风险因素的受试者，以及 14 名来自骨关节炎倡议（OAI）中已确诊的膝关节 OA 受试者。在手动选择半月板内的种子点后，通过高斯拟合模型计算阈值水平。在解剖学、强度和范围约束下，完成阈值操作，然后进行条件扩张和后处理。后处理操作重新评估半月板周围区域中包含和排除的像素，以提高准确性。该方法通过将分割算法结果与来自五位人类读者的手动分割进行比较，对正常和退行性半月板进行了评估。
半自动分割方法产生的结果与受过训练的观察者相似，正常参与者的平均相似度指数超过 0.80，而患有已确诊的膝关节 OA 的参与者，OARSI 关节间隙狭窄（JSN）评分分别为 0、1 和 2 的参与者，相似度指数分别为 0.75、0.67 和 0.64。
与手动分割相比，半自动分割方法在评估轻度至中度 OA 中正常半月板时，产生了准确且一致的半月板分割结果。未来的研究将检查在 OA 的不同阶段，半月板体积、厚度和强度特征的变化。
