Optimal Cell Segmentation and Counting Strategy for Embedding in Low-Power AIoT Devices

This study proposes an end-to-end (E2E) optimization methodology for a white blood cell (WBC) cell segmentation and counting (CSC) pipeline with a focus on deployment to low-power Artificial Intelligence of Things (AIoT) devices. The proposed framework addresses not only the selection of the segmentation model but also the corresponding loss function design, watershed threshold optimization for cell counting, and model compression strategies to balance accuracy, latency, and model size in embedded AIoT applications. For segmentation model selection, UNet, UNet++, ResUNet, EffUNet, FPN, BiFPN, PFPN, Cell-ViT, Evit-UNet and MAXVitUNet were employed, and three types of loss functions—binary cross-entropy (BCE), focal loss, and Dice loss—were utilized for model training. For cell-counting accuracy optimization, a distance transform-based watershed algorithm was applied, and the optimal threshold value was determined experimentally to lie within the range of 0.4 to 0.9. Quantization and pruning techniques were also considered for model compression. Experimental results demonstrate that using an FPN model trained with focal loss and setting the watershed threshold to 0.65 yields the optimal configuration. Compared to the latest baseline techniques, the proposed CSC E2E pipeline achieves a 21.1% improvement in cell-counting accuracy while reducing model size by 74.5% and latency by 16.8% through model compression. These findings verify the effectiveness of the proposed optimization strategy as a lightweight and efficient solution for real-time biomedical applications on low-power AIoT devices.