A Synchronized Layer-by-layer Growing Approach for Plausible Neuronal
  Morphology Generation
Nianzu Yang Kaipeng Zeng Haotian Lu Yexin Wu Zexin Yuan Shengdian Jiang Jiaxiang Wu Yimin Wang Junchi Yan
Neuronal morphology is essential for studying brain functioning and
understanding neurodegenerative disorders. As the acquiring of real-world
morphology data is expensive, computational approaches especially
learning-based ones e.g. MorphVAE for morphology generation were recently
studied, which are often conducted in a way of randomly augmenting a given
authentic morphology to achieve plausibility. Under such a setting, this paper
proposes \textbf{MorphGrower} which aims to generate more plausible morphology
samples by mimicking the natural growth mechanism instead of a one-shot
treatment as done in MorphVAE. Specifically, MorphGrower generates morphologies
layer by layer synchronously and chooses a pair of sibling branches as the
basic generation block, and the generation of each layer is conditioned on the
morphological structure of previous layers and then generate morphologies via a
conditional variational autoencoder with spherical latent space. Extensive
experimental results on four real-world datasets demonstrate that MorphGrower
outperforms MorphVAE by a notable margin. Our code will be publicly available
to facilitate future research.