用混合模型提升细胞牵引力重建精度与泛化能力
Combining Microscopy Data and Metadata for Reconstruction of Cellular Traction Forces Using a Hybrid Vision Transformer-U-Net
- 融合U-Net与视觉变压器，跨尺度建模牵引力场
- 在不同噪声水平下均优于单一模型，准确率显著提升
- 支持加入细胞类型等元数据，适合多实验条件应用
牵引力显微镜（TFM）是量化细胞对周围基质施加力的常用技术。尽管深度学习已用于TFM数据分析，但仍面临跨多空间尺度可靠推断及整合细胞类型等上下文信息以提高准确性的挑战。本文提出ViT+UNet，一种将U-Net与视觉变压器结合的稳健深度学习架构。结果表明，该混合模型在预测牵引力场方面优于独立的U-Net和视觉变压器。此外，ViT+UNet在不同空间尺度和噪声水平下表现出更强的泛化能力，可应用于多种实验设置和成像系统获得的TFM数据集。通过合理构建输入数据，本方法还可引入元数据（如细胞类型信息），提升预测的特异性和准确性。
原文摘要 · Abstract (English)
Traction force microscopy (TFM) is a widely used technique for quantifying the forces that cells exert on their surrounding extracellular matrix. Although deep learning methods have recently been applied to TFM data analysis, several challenges remain-particularly achieving reliable inference across multiple spatial scales and integrating additional contextual information such as cell type to improve accuracy. In this study, we propose ViT+UNet, a robust deep learning architecture that integrates a U-Net with a Vision Transformer. Our results demonstrate that this hybrid model outperforms both standalone U-Net and Vision Transformer architectures in predicting traction force fields. Furthermore, ViT+UNet exhibits superior generalization across diverse spatial scales and varying noise levels, enabling its application to TFM datasets obtained from different experimental setups and imaging systems. By appropriately structuring the input data, our approach also allows the inclusion of metadata, in our case cell-type information, to enhance prediction specificity and accuracy.
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