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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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