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

Journal: Computer methods and programs in biomedicine
PMID: 39236562

Abstract

BACKGROUND AND OBJECTIVE: Tendon segmentation is crucial for studying tendon-related pathologies like tendinopathy, tendinosis, etc. This step further enables detailed analysis of specific tendon regions using automated or semi-automated methods. This study specifically aims at the segmentation of Achilles tendon, the largest tendon in the human body.

Authors

  • Zakia Khatun
    Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Salerno, Italy; Institute of Biomedical and Neural Engineering, Department of Engineering, Reykjavik University, Reykjavik, Iceland. Electronic address: zkhatun@unisa.it.
  • Halldór Jónsson
    Department of Orthopaedics, Landspitali University Hospital, Reykjavik, Iceland.
  • Mariella Tsirilaki
    Department of Radiology, Landspitali University Hospital, Reykjavik, Iceland.
  • Nicola Maffulli
    Department of Musculoskeletal Disorders, School of Medicine and Surgery, University of Salerno, Fisciano, Italy.
  • Francesco Oliva
    Department of Human Sciences and Promotion of the Quality of Life, San Raffaele Roma Open University, Rome, Italy.
  • Pauline Daval
    Biomedical Department, École Polytechnique Universitaire d'Aix-Marseille, Marseille, France.
  • Francesco Tortorella
    Department of Information and Electrical Engineering and Applied Mathematics, University of Salerno, Fisciano, SA 84084, Italy. Electronic address: ftortorella@unisa.it.
  • Paolo Gargiulo
    Institute of Biomedical and Neural Engineering, Department of Engineering, Reykjavik University, Reykjavik, Iceland; Department of Science, Landspitali University Hospital, Reykjavik, Iceland.

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