NeuroSeg-III: efficient neuron segmentation in two-photon Ca2+ imaging data using self-supervised learning.
Biomed Opt Express
; 15(5): 2910-2925, 2024 May 01.
Article
em En
| MEDLINE
| ID: mdl-38855703
ABSTRACT
Two-photon Ca2+ 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 Ca2+ 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 Ca2+ imaging data and delivering remarkable precision in neuron segmentation.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Biomed Opt Express
Ano de publicação:
2024
Tipo de documento:
Article
País de afiliação:
China
País de publicação:
Estados Unidos