Lightweight Instance Segmentation Network for Microscopic Images of Suspension-Cultured B Cells Integrating Edge Priors and Multi-Scale Attention

Precise single-cell analysis is important for life sciences and precision medicine. In antibody drug discovery and immunotherapy, automated segmentation and quantitative analysis of suspension-cultured B cells in label-free bright-field microscopic images can support image-based characterization of cell populations under the recorded culture conditions. However, accurate segmentation remains challenging because of weak boundary gradients, heterogeneous cell sizes, background interference, and high-density clustering. Existing deep learning models may also face a trade-off between segmentation performance and model compactness, which can limit their use in resource-constrained automated microscopy systems. To address these challenges, we propose EPMSA-Net, a lightweight instance segmentation network that integrates edge priors and multi-scale attention for B-cell image analysis. The proposed framework incorporates two complementary structures: the Edge-Aware Feature Preservation (EAFP) module supplements weak boundary-related and local spatial cues during early feature extraction, whereas the Multi-Scale Contextual Attention (MSCA) module subsequently models multi-scale contextual information and recalibrates downstream feature responses. Evaluation on the current suspension-cultured B-cell dataset yielded an F1-score of 94.44% ± 0.53% and an Aggregated Jaccard Index (AJI) of 89.87% ± 0.78% over five independent training runs, with a parameter count of 3.276 M. Density-stratified evaluation showed relatively small F1-score variation across the predefined low-, medium-, and high-density subsets, with a mean CV_F1 of 0.84% ± 0.13%. The resulting instance masks were further used to calculate image-level cell count and segmented area ratio (SAR). These results indicate that EPMSA-Net achieves competitive segmentation performance with a compact model size on the current dataset. Further validation across independent culture batches, imaging systems, and hardware platforms is required to evaluate its broader applicability and deployment performance.