将多帧荧光显微图像融合为高质量图像，提升细胞检测数量44%。
Multi-Temporal Frames Projection for Dynamic Processes Fusion in Fluorescence Microscopy
- 通过多时相帧投影融合技术，整合动态生物图像信息。
- 在心肌细胞数据集上实现44%的细胞计数提升。
- 适合需融合多时序图像的生物成像与分割任务。
荧光显微镜广泛用于活体生物样本分析，但图像常受噪声、时间变异性和信号波动影响。本文提出一种新型计算框架，将多时相帧信息融合为单张高质量图像，同时保留原始视频的生物学内容。我们在包含111种配置的挑战性数据集上评估该方法，该数据集由动态、异质且形态复杂的二维心肌细胞单层构成。结果表明，该框架结合来自不同计算机视觉领域的可解释技术，能生成既保留又增强原始帧质量与信息的合成图像，在细胞计数上相比以往方法平均提升44%。该流水线适用于其他需将多时序图像堆栈融合为高质量二维图像的成像领域，有助于后续标注与分割任务。
原文摘要 · Abstract (English)
Fluorescence microscopy is widely employed for the analysis of living biological samples; however, the utility of the resulting recordings is frequently constrained by noise, temporal variability, and inconsistent visualisation of signals that oscillate over time. We present a unique computational framework that integrates information from multiple time-resolved frames into a single high-quality image, while preserving the underlying biological content of the original video. We evaluate the proposed method through an extensive number of configurations (n = 111) and on a challenging dataset comprising dynamic, heterogeneous, and morphologically complex 2D monolayers of cardiac cells. Results show that our framework, which consists of a combination of explainable techniques from different computer vision application fields, is capable of generating composite images that preserve and enhance the quality and information of individual microscopy frames, yielding 44% average increase in cell count compared to previous methods. The proposed pipeline is applicable to other imaging domains that require the fusion of multi-temporal image stacks into high-quality 2D images, thereby facilitating annotation and downstream segmentation.
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