Deep learning-based fully automatic segmentation of wrist cartilage in MR images

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 twenty multi-slice MRI datasets acquired with two different coils in eleven 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 patch-based U-Net networks. Our PB-CNN also demonstrated a good agreement with manual segmentation (Sorensen-Dice similarity coefficient (DSC) = 0.81) in the representative (central coronal) slices with 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

CNN ‐based fully automatic wrist cartilage volume quantification in MR images: A comparative analysis between different CNN architectures

Purpose Automatic measurement of wrist cartilage volume in MR images. Methods We assessed the performance of four manually optimized variants of the U‐Net architecture, nnU‐Net and Mask R‐CNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patch‐based convolutional neural network (CNN) we previously designed. The segmentation quality was assessed on the basis of a comparative analysis with manual segmentation. The best networks were compared using a cross‐validation approach on a dataset of 33 3D VIBE images of mostly healthy volunteers. Influence of some image parameters on the segmentation reproducibility was assessed. Results The U‐Net‐based networks outperformed the patch‐based CNN in terms of segmentation homogeneity and quality, while Mask R‐CNN did not show an acceptable performance. The median 3D DSC value computed with the U‐Net_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the U‐Net_AL provided the lowest mean volume error (17%) and the highest Pearson correlation coefficient (0.765) with respect to the ground truth values. Of interest, the reproducibility computed using U‐Net_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRI‐based wrist cartilage volume is strongly affected by the image resolution. Conclusions U‐Net CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fine‐tuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a non‐MRI method

Deep learning‐based fully automatic segmentation of wrist cartilage in MR images

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