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.

(2024). Enhanced Attention Res-Unet for Segmentation of Knee Bones [journal article - articolo]. In MATHEMATICS. Retrieved from https://hdl.handle.net/10446/292605

Enhanced Attention Res-Unet for Segmentation of Knee Bones

Ghidotti, Anna;
2024-01-01

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.
articolo
2024
Aibinder, Daniel; Weisberg, Matan; Ghidotti, Anna; Weiss Cohen, Miri
(2024). Enhanced Attention Res-Unet for Segmentation of Knee Bones [journal article - articolo]. In MATHEMATICS. Retrieved from https://hdl.handle.net/10446/292605
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/292605
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