The5 existing nuclei segmentation benchmarks either worked on 2D only or annotated6 a small number of 3D cells, perhaps due to the high cost of 3D annotation for7 large-scale data. To fulfill the critical need, we constructed NIS3D, a 3D, high8 cell density, large-volume, and completely annotated Nuclei Image Segmentation9 benchmark, assisted by our newly designed semi-automatic annotation software.10 NIS3D provides more than 22,000 cells across multiple most-used species in this11 area. Each cell is labeled by three independent annotators, so we can measure the12 variability of each annotation. A confidence score is computed for each cell, allow-13 ing more nuanced testing and performance comparison. A comprehensive review14 on the methods of segmenting 3D dense nuclei was conducted. The benchmark was15 used to evaluate the performance of several selected state-of-the-art segmentation16 algorithms. The best of current methods is still far away from human-level accuracy,17 corroborating the necessity of generating such a benchmark. The testing results18 also demonstrated the strength and weakness of each method and pointed out the19 directions of further methodological development.

Live cell culture is crucial in biomedical studies for analyzing cell properties and dynamics in vitro. This study focuses on segmenting unstained live cells imaged with bright-field microscopy. While many segmentation approaches exist for microscopic images, none consistently address the challenges of bright-field live-cell imaging with high throughput, where temporal phenotype changes, low contrast, noise, and motion-induced blur from cellular movement remain major obstacles. We developed a low-cost CNN-based pipeline incorporating comparative analysis of frozen encoders within a unified U-Net architecture enhanced with attention mechanisms, instance-aware systems, adaptive loss functions, hard instance retraining, dynamic learning rates, progressive mechanisms to mitigate overfitting, and an ensemble technique. The model was validated on a public dataset featuring diverse live cell variants, showing consistent competitiveness with state-of-the-art methods, achieving 93% test accuracy and an average F1-score of 89% (std. 0.07) on low-contrast, noisy, and blurry images. Notably, the model was trained primarily on bright-field images with limited exposure to phase- contrast microscopy (<20%), yet it generalized effectively to the phase-contrast LIVECell dataset, demonstrating modality, robustness and strong performance. This highlights its potential for real- world laboratory deployment across imaging conditions. The model requires minimal compute power and is adaptable using basic deep learning setups such as Google Colab, making it practical for training on other cell variants. Our pipeline outperforms existing methods in robustness and precision for bright-field microscopy segmentation. The code and dataset are available for reproducibility 1.