Enhanced Attention Res-Unet for Segmentation of Knee Bones

Daniel Aibinder, Matan Weisberg, Anna Ghidotti and Miri Weiss Cohen ()
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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

Mathematics, 2024, vol. 12, issue 14, 1-16

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.

Keywords: knee bone segmentation; deep learning; attention U-net (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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