Self-Supervised Learning of Skeleton-Aware Morphological Representation for 3D Neuron Segments

Effective morphological analysis of large-scale 3D neural data plays a crucial role in neuroscience research. However, the ultra-scale data volume from high-resolution microscopy imaging makes manual analysis significantly challenging for 3D rendering, morphological analysis, and morphology-based neuron classification. In this paper, we propose a self-supervised approach to learn skeleton-aware morphological representations from ultra-scale 3D segments to support efficient rendering and morphological analysis. Our approach, named ConSkeletonNet, connects skeleton-aware shape simplification and morphology-based neuron classification to enhance the discriminability of learned morphological representations through multi-task joint training. Through experiments on the neuron segments in a full fly brain EM FAFB-FFN1 and a data volume in the cerebral cortex of a human H01, our ConSkeletonNet shows superiority in learning skeletal-aware morphological representation for both neuron segments skeleton extraction and neuron classification. We also apply our ConSkeleton-Net to the 3D model dataset of man-made objects ShapeNet and achieve state-of-the-art performance in skeleton extraction and shape abstraction.