NIMOSEF: Neural implicit motion and segmentation functions

We present NIMOSEF, a novel unified framework that leverages neural implicit functions for joint segmentation, reconstruction, and displacement field estimation in cardiac magnetic resonance imaging (CMRI). By leveraging on a shared implicit representation for joint segmentation and motion estimation our approach improves spatio-temporal consistency with respect to conventional grid-based convolutional neural networks and implicit segmentation functions. NIMOSEF employs an auto-decoder architecture to learn subject-specific latent representations from unstructured point clouds derived from image intensities and reference segmentations. These latent codes, when combined with 4D space–time coordinates, enable the generation of high-resolution segmentation outputs and smooth, temporally coherent motion estimates. Experimental evaluation on a subset of $700$ random patients from the UK Biobank demonstrates that our method achieves competitive segmentation accuracy—attaining Dice scores of up to $0.93$ for the LV, $0.90$ for the RV and $0.83$ for the LV myocardium, with improved spatio-temporal consistency, predicting a smaller number of disconnected components. Simultaneously, it achieves an average registration error of the whole heart boundary of $3.08 \pm 1.23$mm measured by the Chamfer distance, and $8.57 \pm 4.74$mm according to the $95$th percentile Hausdorff distance. Additionally, feature importance analysis reveals that the learnt implicit representation encodes physiologically relevant information. These results suggest that NIMOSEF offers a promising alternative for high-resolution, temporally consistent cardiac segmentation and motion estimation, with promising potential for advancing clinical assessment of cardiac function.