1 research outputs found
CNN âbased 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 UâNet architecture, nnUâNet and Mask RâCNN frameworks for the segmentation of wrist cartilage. The results were compared to those from a patchâbased 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 crossâvalidation 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 UâNetâbased networks outperformed the patchâbased CNN in terms of segmentation homogeneity and quality, while Mask RâCNN did not show an acceptable performance. The median 3D DSC value computed with the UâNet_AL (0.817) was significantly larger than DSC values computed with the other networks. In addition, the UâNet_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 UâNet_AL was larger than the reproducibility of the manual segmentation. Moreover, the results indicate that the MRIâbased wrist cartilage volume is strongly affected by the image resolution. Conclusions UâNet CNN with attention layers provided the best wrist cartilage segmentation performance. In order to be used in clinical conditions, the trained network can be fineâtuned on a dataset representing a group of specific patients. The error of cartilage volume measurement should be assessed independently using a nonâMRI method