用视觉大模型实现电镜图像中树突的精准分割，提升神经元分析效率。
Segment Anything for Dendrites from Electron Microscopy
- 基于Segment Anything构建视觉基础模型DendriteSAM，捕捉图像全局关系。
- 在健康、病变大鼠及人脑电镜数据上均优于原有和微调模型。
- 首次将视觉大模型用于树突分割，助力神经异常智能诊断。
电子显微镜（EM）图像中细胞结构的分割是分析健康与病变脑组织中神经元和胶质细胞形态的基础。当前神经元分割方法依赖卷积神经网络（CNN），难以有效捕捉图像内的全局关系。本文提出DendriteSAM，一种基于Segment Anything的视觉基础模型，用于电镜图像中树突的交互式与自动分割。该模型在健康大鼠海马区高分辨率电镜数据上训练，并在病变大鼠及人类数据上测试。评估结果表明，相比原始模型及其他微调模型，其掩码质量更优，充分体现了训练中学习到的特征优势。本研究首次实现了视觉基础模型在树突分割中的应用，为神经元异常的计算机辅助诊断开辟了新路径。
原文摘要 · Abstract (English)
Segmentation of cellular structures in electron microscopy (EM) images is fundamental to analyzing the morphology of neurons and glial cells in the healthy and diseased brain tissue. Current neuronal segmentation applications are based on convolutional neural networks (CNNs) and do not effectively capture global relationships within images. Here, we present DendriteSAM, a vision foundation model based on Segment Anything, for interactive and automatic segmentation of dendrites in EM images. The model is trained on high-resolution EM data from healthy rat hippocampus and is tested on diseased rat and human data. Our evaluation results demonstrate better mask quality compared to the original and other fine-tuned models, leveraging the features learned during training. This study introduces the first implementation of vision foundation models in dendrite segmentation, paving the path for computer-assisted diagnosis of neuronal anomalies.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。