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Beyond pixel: Superpixel-based MRI segmentation through traditional machine learning and graph convolutional network.

Zakia KhatunHalldór JónssonMariella TsirilakiNicola MaffulliFrancesco OlivaPauline DavalFrancesco TortorellaPaolo Gargiulo
Published in: Computer methods and programs in biomedicine (2024)
Our proposed pipeline demonstrates the efficacy of employing superpixel generation as a coarse segmentation technique for the final tendon segmentation. Whether utilizing RF, SVM-based superpixel classification, or GCN-based classification for tendon segmentation, our system consistently achieves commendable AUC scores, especially the non-graph-based approach. Given the limited dataset, our graph-based method did not perform as well as non-graph-based superpixel classifications; however, the results obtained provide valuable insights into how well the models can distinguish between tendons and non-tendons. This opens up opportunities for further exploration and improvement.
Keyphrases
  • convolutional neural network
  • deep learning
  • machine learning
  • artificial intelligence
  • neural network
  • magnetic resonance imaging
  • molecular dynamics
  • big data
  • rotator cuff
  • computed tomography
  • magnetic resonance