现代电子技术2025，Vol.48Issue(5)：36-42,7.DOI:10.16652/j.issn.1004-373x.2025.05.006
基于VMF-UNet的液基细胞制染机缺陷图像分割
VMF-UNet based image segmentation of defects in liquid-based cell preparation staining machine
摘要
Abstract
A VMF-UNet model is proposed to identify the defects in the preparation effect of liquid-based cell preparation staining machine and compensate for its final preparation rate.This model is based on the UNet.The convolutional part of VGG16Net is used to replace the encoder part of UNet neural network.A multi-scale efficient local attention(MELA)mechanism is added and a feature refinement module(FRM)is introduced,which aims to eliminate the image over-segmentation,image under-segmentation,unclear edges of preparation defect areas,and limited field of view of the UNet model.The experiments are based on the principle of medical testing availability.The standard labels of the dataset segmented by the microscope are used as the ″golden standard″.The experiments based on the self-built liquid-based cell preparation defect area image dataset show that the improved network has a mean intersection over union(MIoU),mean pixel accuracy(MPA),F1-score,and accuracy rate of 82.73%,93.56%,81.93% and 96.10% during segmentation.The experimental results demonstrate that the VMF-UNet model has a better segmentation effect on the preparation defect areas of the liquid-based cell preparation staining machine,and can effectively compensate for the final preparation rate of the machine,providing effective basis for equipment reprocessing and improving its applicability.关键词
深度学习/语义分割/UNet/注意力机制/缺陷检测/液基细胞制染机Key words
deep learning/semantic segmentation/UNet/attention mechanism/defect detection/liquid-based cell preparation staining machine分类
电子信息工程引用本文复制引用
田文豪,汪繁荣,乔一航..基于VMF-UNet的液基细胞制染机缺陷图像分割[J].现代电子技术,2025,48(5):36-42,7.基金项目
国家自然科学基金项目(61903129) （61903129）
