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Enhanced Attention Res-Unet for Segmentation of Knee Bones

Author

Listed:
  • Daniel Aibinder

    (Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel)

  • Matan Weisberg

    (Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel)

  • Anna Ghidotti

    (Department of Management, Information and Production Engineering (DIGIP), University of Bergamo, Viale G. Marconi, 24044 Dalmine, BG, Italy)

  • Miri Weiss Cohen

    (Department of Software Engineering, Braude College of Engineering, Karmiel 2161002, Israel)

Abstract

The objective of this study was to develop a U-net capable of generating highly accurate 3D models of knee bones, in particular the femur. As part of the approach, a U-net was designed, trained, and validated. In order to achieve these goals, a novel architecture was proposed, including an architecture that reduces encoder parameters and incorporates transfer learning, in order to enhance the attention U-net. Additionally, an extra depth layer was added to extract more salient information. Moreover, the model includes a classifier unit to reduce false positives, as well as a Tversky focal loss function, which is an innovative loss function. The proposed architecture achieved a Dice coefficient of 98.05. By using these enhanced tools, clinicians can visualize and analyze knee structures more accurately, improve surgical intervention effectiveness, and improve patient care quality overall.

Suggested Citation

  • Daniel Aibinder & Matan Weisberg & Anna Ghidotti & Miri Weiss Cohen, 2024. "Enhanced Attention Res-Unet for Segmentation of Knee Bones," Mathematics, MDPI, vol. 12(14), pages 1-16, July.
  • Handle: RePEc:gam:jmathe:v:12:y:2024:i:14:p:2284-:d:1440162
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