A time series image semantic segmentation model modified by optical flow
The development of medical imaging technology has generated a massive amount of medi-cal image data,which reflects the internal structural features of the human body.Medical image seg-mentation 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,name-ly Dice similarity,pixel accuracy and cross-merge ratio,on different types of datasets,namely Drosoph-ila electron micrographs,combined healthy abdominal organ segmentation and coronary angiogram,which validate the effectiveness and generalization of the proposed model.
万方数据
维普
