NIMOSEF: Neural Implicit Motion and Segmentation Functions

Jaume Banus, Antoine Delaloye, Pedro M. Gordaliza, Costa Georgantas, Ruud B. van Heeswijk, Jonas Richiardi

Abstract: 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 95th 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.

External IDs:dblp:conf/miccai/BanusDGGHR25