NeuroSeg-III: efficient neuron segmentation in two-photon Ca 2+imaging data using self-supervised learning

Two -photon Ca 2 + imaging technology increasingly plays an essential role in neuroscience research. However, the requirement for extensive professional annotation poses a significant challenge to improving the performance of neuron segmentation models. Here, we present NeuroSeg-III, an innovative self -supervised learning approach specifically designed to achieve fast and precise segmentation of neurons in imaging data. This approach consists of two modules: a self -supervised pre -training network and a segmentation network. After pre -training the encoder of the segmentation network via a self -supervised learning method without any annotated data, we only need to fine-tune the segmentation network with a small amount of annotated data. The segmentation network is designed with YOLOv8s, FasterNet, efficient multi -scale attention mechanism (EMA), and bi-directional feature pyramid network (BiFPN), which enhanced the model's segmentation accuracy while reducing the computational cost and parameters. The generalization of our approach was validated across different Ca 2 + indicators and scales of imaging data. Significantly, the proposed neuron segmentation approach exhibits exceptional speed and accuracy, surpassing the current state-of-the-art benchmarks when evaluated using a publicly available dataset. The results underscore the effectiveness of NeuroSeg-III, with employing an efficient training strategy tailored for two -photon Ca 2 + imaging data and delivering remarkable precision in neuron segmentation. (c) 2024 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement