现代信息科技2025，Vol.9Issue(6)：105-109,115,6.DOI:10.19850/j.cnki.2096-4706.2025.06.020
SAMG:一种结合SAM和CNN的睑板腺图像分割模型
SAMG:A Meibomian Gland Image Segmentation Model Combining SAM and CNN
摘要
Abstract
SAM is a well-known general image segmentation model.Although SAM performs well on natural images,its performance is significantly degraded and its generalization ability is limited when processing meibomian gland medical images with low contrast and blurred boundaries.Therefore,this paper proposes a model SAMG suitable for meibomian gland image segmentation.Based on the image encoder of SAM,the model adds a CNN branch and connects the CNN branch with the original image encoder(ViT branch)through a cross-attention module.In addition,in the final part of the encoder,a SE module enables the model to understand the image content at a finer granularity.This model structure design makes the additional parameters and the amount of calculation relatively small.With the support of professional meibomian gland medical image dataset,comparative experiments show that the SAMG model performs well in meibomian gland image segmentation tasks and can be deployed on entry-level GPU.关键词
SAM/CNN/ViT/图像分割Key words
SAM/CNN/ViT/image segmentation分类
信息技术与安全科学引用本文复制引用
张宸睿,李秀丽..SAMG:一种结合SAM和CNN的睑板腺图像分割模型[J].现代信息科技,2025,9(6):105-109,115,6.基金项目
2024年度河南省高等教育教学改革研究与实践项目(2024SJGLX0332) （2024SJGLX0332）
2023年河南省高等教育教学改革研究与实践项目研究生教育类(2023SJGLX112Y) （2023SJGLX112Y）
