Segmentation of Neuroblastoma with PET-CT Multimodal Images Based on Tumor Shape Characteristics and Point Clouds Method
The automatic tumor segmentation of PET-CT images through intelligent learning methods is an important research field to assist doctors in formulating diagnosis and treatment plans.PET-CT images have the advantages of both PET and CT modalities.Traditional methods mostly simply register and fuse the images of the two modalities to extract features,ignoring the irregular tumor boundary contour of neuroblas-toma.To this end,a two-stage automatic segmentation framework structure model is proposed.Firstly,use 3D convolutional neural networks to locate the tumor location;Then generate multimodal point cloud data near the segmented tumor area and extract the shape contour features of the tumor;Finally,the features extracted by the two networks are fused to predict the final segmentation result.The proposed model was compared with other multimodal methods on both proprietary and public datasets,and the experimental results verified the superiority and ef-fectiveness of the proposed model,which can provide reference and inspiration for researchers studying the segmentation of neuroblastoma.
deep learningpoint cloudsmultimodalmedical image segmentationPET-CT
万方数据
维普
