A resource-efficient deep learning pipeline for brightfield live-cell segmentation under constrained computational environments

Automated segmentation of unstained live cells in bright-field microscopy remains challenging for resource-constrained laboratories due to limited annotated data, computational resources, low contrast, motion blur, and overlapping cellular structures. We present a resource-efficient CNN pipeline comparing six frozen encoder backbones within enhanced U-Net architectures incorporating attention mechanisms, instance-aware modules, composite loss functions, hard-example mining, and 14-model ensemble voting. Trained on 857 bright-field and phase-contrast images expanded to 17,997 samples through augmentation, the framework required 6.5 hours on a Google Colab T4 GPU. External validation on LIVECell achieved Dice and F1 scores of 0.89, demonstrating strong cross-modality generalisation. On challenging bright-field images, the framework outperformed CellPose-SAM and StarDist while requiring substantially lower computational resources than foundation-model-based approaches. The proposed pipeline provides a reproducible and cost-effective solution for robust live-cell segmentation under constrained computational environments. 1