Detection of cartilage loss is crucial for the diagnosis of osteo- and rheumatoid arthritis. An automatic tool for wrist cartilage segmentation may be of high interest as the corresponding manual procedure is tedious. U-Net is a convolution neural network, which has been largely used for biomedical images, but its performance in segmenting wrist cartilage images is modest. Here, we assessed whether adding attention layers to U-Net architecture would improve the segmentation performance. A truncated version of U-Net with attention layers showed the best performance(3D DSC - 0.811), as well as in the accuracy of cartilage cross-section measurements (bias - 6.4 mm2).