Ekaterina Brui, Aleksandr Y. Efimtcev, Vladimir A. Fokin, Remi Fernandez, Anatoliy G. Levchuk, Augustin C. Ogier, Alexey A. Samsonov, Jean P. Mattei, Irina V. Melchakova, David Bendahan, Anna Andreychenko, Deep learning‐based fully automatic segmentation of wrist cartilage in MR images, NMR in Biomedecine, May 2020
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.
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