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Abstract #2969

Wrist cartilage segmentation using U-Net convolutional neural networks

Nikita Vladimirov1 and Ekaterina A. Brui1
1Department of Physics and Engineering, ITMO University, Saint Petersburg, Russian Federation

Segmentation of wrist cartilage may be of interest for the detection of cartilage loss during osteo- and rheumatoid arthritis. In this work, U-Net convolutional neural networks were used for automatic wrist cartilage segmentation. The networks were trained on a limited amount of labeled data (10 3D VIBE images). The results were compared with the previously published for a planar patch-based archutecture (3D DSC = 0.71). Utilisation of U-Net archutecture and data augmentation allowed to significantly increase the segmentation accuracy in lateral slices. Truncation of the deepest level in the classical U-Net architecture provided the 3D DSC=0.77.

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