A Semi-supervised Pipeline for Accurate Neuron Segmentation with Fewer Ground Truth Labels.

ABSTRACT: Recent advancements in two-photon calcium imaging have enabled scientists to record the activity of thousands of neurons with cellular resolution. This scope of data collection is crucial to understanding the next generation of neuroscience questions, but analyzing these large recordings requires automated methods for neuron segmentation. Supervised methods for neuron segmentation achieve state of-the-art accuracy and speed but currently require large amounts of manually generated ground truth training labels. We reduced the required number of training labels by designing a semi-supervised pipeline. Our pipeline used neural network ensembling to generate pseudolabels to train a single shallow U-Net. We tested our method on three publicly available datasets and compared our performance to t

SUBMITTER: Baker CM 

PROVIDER: S-EPMC10880440 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature