Automated analysis of data with arbitrarity in intensity, protocols, and field-of-view is an important task in the modern biomedical imaging pipeline. Recently, spiking computation has been leveraged to reduce the computational overhead of neural networks in the domain of medicine. However, state-of-the-art methods are on the trend of energy for accuracy, a lack of scalability due to the non-differentiable activations of spiking neurons, shallow architectures, and unsophisticated tasks. To avoid native spiking neural network (SNN) design, a pipeline for end-to-end vertebrae segmentation, identification, and localization from Computed Tomography acquisitions is denoted wherein the learned parameters of deep convolutional analog networks are transferred to equivalent-accurate spiking ones. We report that complex architectures such as autoencoders can be run as rate-based SNNs with a major reduction in latency to achieve error rates close to conventional deep methods. The networks are also able to forego normalization to a common space, making our approach ideal in an intraoperative context and for longitudinal neuroanatomical studies. To the best of our knowledge, this paper is the first to study biologically-inspired clinical systems on such massive heterogeneous data.