How Deep Learning Is Transforming Brain Mapping

How Deep Learning Is Transforming Brain Mapping

Shelly Fan

Unfortunately, microscopic neuroimages look nothing like the brain anatomy coloring books. Rather, they come in a wide variety of sizes, rotations, and colors. The imaged brain sections, due to extensive chemical pre-treatment, are often distorted or missing pieces. To ensure labeling accuracy, scientists often have to go in and hand-annotate every single image. Similar to the pain of manually labeling data for machine learning, this step creates a time-consuming, labor-intensive bottleneck in neuro-cartography endeavors.

No more. This month, a team from the Brain Research Institute of UZH in Zurich tapped the processing power of artificial brains to take over the much-hated job of “region segmentations.” The team fed a deep neural net microscope images of whole mouse brains, which were “stained” with a variety of methods and a large pool of different markers.

Regardless of age, method, or marker, the algorithm reliably identified dozens of regions across the brain, often matching the performance of human annotation. The bot also showed a remarkable ability to “transfer” its learning: trained on one marker, it could generalize to other markers or staining. When tested on a pool of human brain scans, the algorithm performed just as well.

“Our…method can accelerate brain-wide exploration of region-specific changes in brain development and, by easily segmenting brain regions of interest for high-throughput brain-wide analysis, offer an alternative to existing complex … techniques,” the authors said.