基于 YOLOv11 模型实现高通量血小板细胞图像精准分割OA

Objective To develop an automated platelet instance segmentation method for high-throughput structured il-lumination microscopy(SIM)data,with the goal of improving segmentation accuracy in wide-field bright-field images.Methods Bright-field images were used as input to build a YOLOv11-based unified detection-and-segmentation network,trained and inferred with a standardized annotation format.The network consists of a backbone for feature extraction,an im-proved PAFPN neck for multi-scale feature fusion,and a joint detection/segmentation prediction head,enhancing represen-tation of small targets and crowded scenes.Training was initialized with pretrained weights,using an input resolution of 1 024×1 024 for 200 epochs with a batch size of 4 and automatic mixed precision enabled.Pixels outside the 1st and 99th grayscale percentiles were clipped and linearly mapped to 8-bit intensity values.Data augmentation included flipping,rota-tion,translation,and scaling.The loss function combined binary cross-entropy loss and Dice loss.Results In five-fold cross-validation on the gastric cancer dataset,YOLOv11-m achieved balanced precision and recall,with an mAP50-95 of 0.848.Compared with Mask R-CNN,YOLOv8-m,YOLOv9-c,and YOLO26-m,it used a smaller model size and achieved a total processing time of 14.85 ms per image.When directly transferred to validation sets of cholangiocarcinoma,hepato-cellular carcinoma,liver cirrhosis,and ovarian cancer,the model maintained stable detection and segmentation perform-ance,indicating good generalization across data sources and imaging variations.Conclusion The proposed method enables high-throughput,robust platelet instance segmentation and effectively handles challenging bright-field scenarios involving small targets,dense distributions,and local overlaps.By balancing segmentation accuracy and inference efficiency,it serves as a reliable preprocessing component in automated platelet image-analysis pipelines.