Developing Brain Atlas through Deep Learning
This provides an automated alternative to complex brain registration techniques for neuroscientists, accelerating brain-wide exploration of region-specific changes in development.
The paper tackles the challenge of variability in brain size and form for creating standard brain reference atlases by introducing SeBRe, a fully automated deep neural network-based method for segmenting brain regions with minimal human supervision. The method was validated on mouse brain images across developmental time points, neuronal markers, and imaging modalities, and also assessed on MR-scanned human brains.
Neuroscientists have devoted significant effort into the creation of standard brain reference atlases for high-throughput registration of anatomical regions of interest. However, variability in brain size and form across individuals poses a significant challenge for such reference atlases. To overcome these limitations, we introduce a fully automated deep neural network-based method (SeBRe) for registration through Segmenting Brain Regions of interest with minimal human supervision. We demonstrate the validity of our method on brain images from different mouse developmental time points, across a range of neuronal markers and imaging modalities. We further assess the performance of our method on images from MR-scanned human brains. Our registration method can accelerate brain-wide exploration of region-specific changes in brain development and, by simply segmenting brain regions of interest for high-throughput brain-wide analysis, provides an alternative to existing complex brain registration techniques.