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

Behnaz Gheflati¹, Morteza Mirzaei², Sunil Rottoo², Hassan Rivaz³

¹Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada. b_ghefla@encs.concordia.ca.

²Think Surgical Inc., Montreal, QC, Canada.

³Department of Electrical and Computer Engineering, Concordia University, Montreal, QC, Canada.

PubMed DOI

Abstract

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 parameterized SSM (DL-ANATSSM) by introducing a no…

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