  Over the past decade, artificial neural networks (ANNs) have made tremendous
advances, in part due to the increased availability of annotated data. However,
ANNs typically require significant power and memory consumptions to reach their
full potential. Spiking neural networks (SNNs) have recently emerged as a
low-power alternative to ANNs due to their sparsity nature. SNN, however, are
not as easy to train as ANNs. In this work, we propose a hybrid SNN training
scheme and apply it to segment human hippocampi from magnetic resonance images.
Our approach takes ANN-SNN conversion as an initialization step and relies on
spike-based backpropagation to fine-tune the network. Compared with the
conversion and direct training solutions, our method has advantages in both
segmentation accuracy and training efficiency. Experiments demonstrate the
effectiveness of our model in achieving the design goals.
