光流法修正的时序图像语义分割模型

A time series image semantic segmentation model modified by optical flow

医学成像技术的发展带来了海量的医学图像数据,这些图像反映了生物体的内部结构特征,医学图像分割技术可以提高医疗人员的诊断效率,从而成为现代医疗诊断的重要辅助手段之一。然而成像过程中不可避免地会出现噪声或伪影,它们给分割工作带来了极大的挑战。现有的分割模型中,单帧医学图像语义分割模型未考虑图像帧与帧之间的关系,视频语义分割模型虽利用了时序信息,但在边缘提取上有所欠缺。为了解决以上问题,提出了一种以U-Net为骨干,用光流法进行修正的时序语义分割模型。该模型能够提取视频前后帧之间的光流信息,并对当前帧与光流进行特征提取与权重分配,以达到修正的效果。实验结果表明,在果蝇电镜图、腹部综合器官图和冠状动脉造影图这些不同类型的数据集上,该模型在相似性系数、像素准确率和交并比这3个评价指标上都获得了最优结果,验证了所提模型的有效性和泛化性。

The development of medical imaging technology has generated a massive amount of medical image data,which reflects the internal structural features of the human body.Medical image segmentation technology can improve the efficiency of medical diagnosis,making it an important assistive tool for modern medical diagnosis.However,noise or artifacts that are inevitably present in the imaging process bring great challenges to the segmentation work.In existing segmentation models,single-frame medical image semantic segmentation models do not consider the relationship between image frames,while video semantic segmentation models utilize temporal information but have some limitations in edge extraction.To address these issues,this paper proposes a U-Net-based temporal semantic segmentation model modified by optical flow.This model can extract optical flow information between consecutive frames and perform feature extraction and weight allocation on the current frame and optical flow for correction.Experiments show that the model obtains optimal results on three evaluation metrics,namely Dice similarity,pixel accuracy and cross-merge ratio,on different types of datasets,namely Drosophila electron micrographs,combined healthy abdominal organ segmentation and coronary angiogram,which validate the effectiveness and generalization of the proposed model.