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This research introduces an advanced approach to automate the segmentation and quantification of nuclei in fluorescent images through deep learning techniques. Overcoming inherent challenges such as variations in pixel intensities, noisy boundaries, and overlapping edges, our devised pipeline integrates the U-Net architecture with state-of-the-art CNN models, such as EfficientNet. This fusion maintains the efficiency of U-Net while harnessing the superior capabilities of EfficientNet. Crucially, we exclusively utilize high-quality confocal images generated in-house for model training, purposefully avoiding the pitfalls associated with publicly available synthetic data of lower quality. Our training dataset encompasses over 3000 nuclei boundaries, which are meticulously annotated manually to ensure precision and accuracy in the learning process. Additionally, post-processing is implemented to refine segmentation results, providing morphological quantification for each segmented nucleus. Through comprehensive evaluation, our model achieves notable performance metrics, attaining an F1-score of 87% and an Intersection over Union (IoU) value of 80%. Furthermore, its robustness is demonstrated across diverse datasets sourced from various origins, indicative of its broad applicability in automating nucleus extraction and quantification from fluorescent images. This innovative methodology holds significant promise for advancing research efforts across multiple domains by facilitating a deeper understanding of underlying biological processes through automated analysis of fluorescent imagery.