RETINA: Reconstruction-based pre-trained enhanced TransUNet for electron microscopy segmentation on the CEM500K dataset.

IF 3.8 2区生物学 Q1 BIOCHEMICAL RESEARCH METHODS

PLoS Computational Biology Pub Date : 2025-05-28 DOI:10.1371/journal.pcbi.1013115

Cheng Xing, Ronald Xie, Gary D Bader

引用次数: 0

Abstract

Electron microscopy (EM) has revolutionized our understanding of cellular structures at the nanoscale. Accurate image segmentation is required for analyzing EM images. While manual segmentation is reliable, it is labor-intensive, incentivizing the development of automated segmentation methods. Although deep learning-based segmentation has demonstrated expert-level performance, it lacks generalizable performance across diverse EM datasets. Current approaches usually use either convolutional or transformer-based neural networks for image feature extraction. We developed the RETINA method, which combines pre-training on the large, unlabeled CEM500K EM image dataset with a hybrid neural-network model architecture that integrates both local (convolutional layer) and global (transformer layer) image processing to learn from manual image annotations. RETINA outperformed existing models on cellular structure segmentation on five public EM datasets. This improvement works toward automated cellular structure segmentation for the EM community.

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视网膜：基于重建的预训练增强TransUNet，用于CEM500K数据集上的电子显微镜分割。

电子显微镜（EM）彻底改变了我们对纳米级细胞结构的理解。EM图像分析需要精确的图像分割。虽然人工分割是可靠的，但它是劳动密集型的，激励了自动分割方法的发展。尽管基于深度学习的分割已经证明了专家级的性能，但它缺乏跨不同EM数据集的通用性能。目前的方法通常使用卷积或基于变换的神经网络进行图像特征提取。我们开发了RETINA方法，该方法将大型未标记的CEM500K EM图像数据集的预训练与混合神经网络模型架构相结合，该模型架构集成了局部（卷积层）和全局（变压器层）图像处理，以从手动图像注释中学习。在5个公开的EM数据集上，视网膜在细胞结构分割上优于现有模型。这一改进为EM社区实现了自动细胞结构分割。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

PLoS Computational Biology BIOCHEMICAL RESEARCH METHODS-MATHEMATICAL & COMPUTATIONAL BIOLOGY

CiteScore

7.10

自引率

4.70%

发文量

820

审稿时长

2.5 months

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