用YOLOv8自动分析荧光显微镜下的神经元形态，准确率达97%以上。
Automated Morphological Analysis of Neurons in Fluorescence Microscopy Using YOLOv8
- 基于YOLOv8和人工标注数据训练，实现神经元实例分割。
- 形态测量整体准确率达75.32%，关键特征包括长度、面积、灰度值等。
- 适合需要高通量、精准神经元分析的脑科学与细胞成像研究者。
在荧光显微镜图像中对神经元进行精确分割与形态学分析，是神经科学和生物医学成像中的关键步骤。然而，该过程耗时且依赖大量人工操作与专业经验。本文提出一种基于干细胞来源神经元高分辨率数据集的神经元实例分割与测量流水线。采用在人工标注图像上训练的YOLOv8模型，分割准确率超过97%。同时，利用真实标签与预测掩码提取具有生物学意义的特征，如细胞长度、宽度、面积及灰度强度值。所提取形态测量的整体准确率达到75.32%，验证了该方法的有效性。该集成框架为细胞成像与神经科学研究提供了自动化分析工具，减少人工标注需求，支持可扩展的神经元形态精确量化。
原文摘要 · Abstract (English)
Accurate segmentation and precise morphological analysis of neuronal cells in fluorescence microscopy images are crucial steps in neuroscience and biomedical imaging applications. However, this process is labor-intensive and time-consuming, requiring significant manual effort and expertise to ensure reliable outcomes. This work presents a pipeline for neuron instance segmentation and measurement based on a high-resolution dataset of stem-cell-derived neurons. The proposed method uses YOLOv8, trained on manually annotated microscopy images. The model achieved high segmentation accuracy, exceeding 97%. In addition, the pipeline utilized both ground truth and predicted masks to extract biologically significant features, including cell length, width, area, and grayscale intensity values. The overall accuracy of the extracted morphological measurements reached 75.32%, further supporting the effectiveness of the proposed approach. This integrated framework offers a valuable tool for automated analysis in cell imaging and neuroscience research, reducing the need for manual annotation and enabling scalable, precise quantification of neuron morphology.
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