  Detection of cartilage loss is crucial for the diagnosis of osteo- and
rheumatoid arthritis. A large number of automatic segmentation tools have been
reported so far for cartilage assessment in magnetic resonance images of large
joints. As compared to knee or hip, wrist cartilage has a more complex
structure so that automatic tools developed for large joints are not expected
to be operational for wrist cartilage segmentation. In that respect, a fully
automatic wrist cartilage segmentation method would be of high clinical
interest. We assessed the performance of four optimized variants of the U-Net
architecture with truncation of its depth and addition of attention layers
(U-Net_AL). The corresponding 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 using several morphological (2D DSC, 3D DSC, precision) and a
volumetric metrics. The four networks outperformed the patch-based CNN in terms
of segmentation homogeneity and quality. The median 3D DSC value computed with
the U-Net_AL (0.817) was significantly larger than the corresponding DSC values
computed with the other networks. In addition, the U-Net_AL CNN provided the
lowest mean volume error (17%) and the highest Pearson correlation coefficient
(0.765) with respect to the ground truth. Of interest, the reproducibility
computed from using U-Net_AL was larger than the reproducibility of the manual
segmentation. U-net convolutional neural network with additional attention
layers provides 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.
