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Statistical shape model-based estimation of registration error in computer-assisted total knee arthroplasty

Author(s): Gheflati B; Mirzaei M; Zuhars J; Rottoo S; Rivaz H;

Purpose: Computer-assisted surgical navigation systems have been developed to improve the precision of total knee arthroplasty (TKA) by providing real-time guidance on implant alignment relative to patient anatomy. However, surface registration remains a key source of error that can propagate through the surgical workflow. This study investigates how pati ...

Article GUID: 41495592


Leveraging deep learning for nonlinear shape representation in anatomically parameterized statistical shape models

Author(s): Gheflati B; Mirzaei M; Rottoo S; Rivaz H;

Purpose: Statistical shape models (SSMs) are widely used for morphological assessment of anatomical structures. However, a key limitation is the need for a clear relationship between the model's shape coefficients and clinically relevant anatomical parameters. To address this limitation, this paper proposes a novel deep learning-based anatomically par ...

Article GUID: 39953355


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