Scaling Synthetic Brain Data Generation

Abstract

The limited availability of diverse, high-quality datasets is a significant challenge in applying deep learning to neuroimaging research. Although synthetic data generation can potentially address this issue, on-the-fly generation is computationally demanding, while training on pre-generated data is inflexible and may incur high storage costs. We introduce Wirehead, a scalable in-memory data pipeline that significantly improves the performance of on-the-fly synthetic data generation for deep learning in neuroimaging. Wirehead's architecture decouples data generation from training by running multiple generators in independent parallel processes, facilitating near-linear performance gains proportional to the number of generators used. It efficiently handles terabytes of data using MongoDB, greatly minimizing prohibitive storage costs. The robust, modular design enables flexible pipeline configurations and fault-tolerant operation. We evaluated Wirehead with SynthSeg, a synthetic brain segmentation data generation tool that requires 7 days to train a model. When deployed in parallel, Wirehead achieved a near-linear 15.7x increase in throughput with 16 generators. With 20 generators, we can train a model in 9 hours instead of 7 days. This demonstrates Wirehead's ability to greatly accelerate experimentation cycles. While Wirehead represents a substantial step forward, it also reveals opportunities for future research in optimizing generation-training balance and resource allocation. Its ability to facilitate distributed deep learning has significant implications for enabling more ambitious neuroimaging research.