We present a frameworkfor multi-label statistical shape modelling using implicit neural representations (INRs). By training a generalised INR alongside instance-specific latent codes to model individual shapes as continuous signed distance maps, the approach captures complex anatomical variations without relying on fixed landmarks. We further propose to employ the shape model for regularisation to obtain robust reconstructions of corrupted or incomplete data. Experiments on 2D chest X-ray segmentations demonstrate that this regularisation facilitates reconstructions under flawed data conditions, achieving highfidelity segmentations. Conceptual examples highlight topological advantages of INR-based shape models over conventional point distribution models.