The study objective was to investigate the performance of a dedicated convolutional neural network (CNN) optimized for wrist cartilage segmentation from 2D MR images. CNN utilized a planar architecture and patch‐based (PB) training approach that ensured optimal performance in the presence of a limited amount of training data. The CNN was trained and validated in 20 multi‐slice MRI datasets acquired with two different coils in 11 subjects (healthy volunteers and patients). The validation included a comparison with the alternative state‐of‐the‐art CNN methods for the segmentation of joints from MR images and the ground‐truth manual segmentation. When trained on the limited training data, the CNN outperformed significantly image‐based and PB‐U‐Net networks. Our PB‐CNN also demonstrated a good agreement with manual segmentation (Sørensen–Dice similarity coefficient [DSC] = 0.81) in the representative (central coronal) slices with a large amount of cartilage tissue. Reduced performance of the network for slices with a very limited amount of cartilage tissue suggests the need for fully 3D convolutional networks to provide uniform performance across the joint. The study also assessed inter‐ and intra‐observer variability of the manual wrist cartilage segmentation (DSC = 0.78‐0.88 and 0.9, respectively). The proposed deep learning‐based segmentation of the wrist cartilage from MRI could facilitate research of novel imaging markers of wrist osteoarthritis to characterize its progression and response to therapy.

中文翻译：

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基于深度学习的 MR 图像中腕部软骨的全自动分割。

研究目的是研究针对 2D MR 图像的腕部软骨分割而优化的专用卷积神经网络 (CNN) 的性能。CNN 采用平面架构和基于补丁 (PB) 的训练方法，确保在有限训练数据的情况下实现最佳性能。CNN 在 20 个多层 MRI 数据集中进行了训练和验证，这些数据集是使用 11 名受试者（健康志愿者和患者）的两个不同线圈采集的。验证包括与用于从 MR 图像中进行关节分割的最先进的 CNN 方法和真实手动分割进行比较。当在有限的训练数据上进行训练时，CNN 的性能明显优于基于图像的网络和 PB-U-Net 网络。我们的 PB-CNN 还在具有大量软骨组织的代表性（中央冠状）切片中证明了与手动分割（Sørensen-Dice 相似系数 [DSC] = 0.81）的良好一致性。软骨组织数量非常有限的切片的网络性能下降表明需要完全 3D 卷积网络来提供跨关节的统一性能。该研究还评估了手动腕部软骨分割的观察者间和观察者内的变异性（DSC 分别 = 0.78-0.88 和 0.9）。所提出的基于深度学习的 MRI 腕部软骨分割可以促进腕部骨关节炎新型成像标记物的研究，以表征其进展和对治疗的反应。