Total Metabolic Tumor Volume Grading Model Based on Image Reconstruction
Total Metabolic Tumor Volume(TMTV)is an important prognostic indicator independent of other indicators,and it has important guiding role in the accurate treatment on patients.Accurately determining the grading of TMTV is a highly challenging task.Therefore,this paper proposes a total metabolic tumor volume grading model based on image reconstruction,which includes two modules:Segmentation Assisted Multidimensional Feature Learning Module(SAMFL)and Reconstruction Correction Module(RCM).The former obtains more accurate TMTV by optimizing and fusing segmentation features,the latter uses image reconstruction and deviation correction methods to correct the areas that were not accurately recognized in segmentation,thereby further improving the accuracy of TMTV.On the dataset of the University of Chicago Hospital,the accuracy of the model reaches 71%.Compared with other methods,this model performs better in TMTV grading.
total metabolic tumor volumeimage reconstructionimage segmentationdeviation correction
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