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.