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

Nikita Vladimirov, Ekaterina Brui, Anatoliy Levchuk, Walid Al-Haidri, Vladimir Fokin, Aleksandr Efimtcev, David Bendahan*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish
Pages (from-to)737-751
Number of pages15
JournalMagnetic Resonance in Medicine
Issue number2
StatePublished - Aug 2023
Externally publishedYes


  • MRI
  • arthritis
  • cartilage
  • deep learning
  • segmentation
  • wrist


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