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Fluorescence time–lapse microscopy is a fundamental tool for investigating dynamic cellular processes, yet automatic cell detection and temporal signal extraction remain strongly dependent on intensity–driven thresholds and rigid decision rules. Such approaches may become unstable in the presence of noise, photobleaching, overlapping structures, and intrinsic biological variability. In this work, we introduce an uncertainty–aware computational framework that preserves the classical analysis workflow—temporal projection, peak detection, and region–of–interest (ROI) signal extraction—while extending it through neutrosophic morphological enhancement. The proposed implementation emphasizes practical reproducibility and flexibility: the analysis pipeline can automatically operate on heterogeneous inputs, including single images, temporal projections, full 3D stacks, or image sequences, while maintaining consistent detection parameters across classical and neutrosophic processing. By spatially flattening the image stack and enhancing the resulting activity landscape through neutrosophic morphology, the framework explicitly models reliable information, ambiguity, and background contributions, allowing weak but spatially coherent structures to emerge as stable candidates for detection. Beyond detection, the method extracts temporal ROI signals and provides detailed visualization and computational reporting, enabling a deeper interpretation of dynamic cellular behaviour. The framework remains fully compatible with standard fluorescence analysis pipelines while improving robustness, interpretability, and reproducibility under challenging imaging conditions, offering a principled extension for analysing uncertain or borderline cellular signals in fluorescence time–lapse microscopy.