Research On Few-Shot Segmentation For Brain Tumor Images

Brain tumors are a group of abnormal cells that grow in the brain.There are two main types: primary brain tumors and metastatic brain tumors.Glioma is one of the most common primary brain tumor with several different aggressiveness and different characteristics.Histology sub-area.According to the latest statistics from the National Cancer Report,the incidence of brain tumors in China is about 7 to 8 per 100,000.Although the annual growth rate is only about 1%,it also maintains a slow growth trend.Brain tumors are a great threat to human health.They can damage and compress normal brain tissues,cause brain edema,increase intracranial pressure,and affect the respiratory central nervous system,and ultimately lead to death.Therefore,brain tumors are still a topic that cannot be ignored in the medical field.In the diagnosis process of brain tumors,MRI is often used to show the structure of the brain,because the brightness of malignant tumors can be enhanced by enhancers,etc.,which can diagnose brain tumors earlier and more accurately.However,in order to completely segment the various histological sub-regions of brain tumors,it is necessary to combine the characteristics of multiple modalities in MR images.Brain tumor image segmentation is a major content in the process of brain tumor treatment.Currently,doctors rely on manual segmentation.A large amount of brain tumor image data brings great pressure to doctors.Manual segmentation is time-consuming and labor-intensive,and its effect depends on Based on the doctor’s experience,the study of automatic segmentation methods is of great significance to assist the doctor’s diagnosis.At present,deep convolutional neural networks have made great progress in the semantic segmentation of medical images,but generally require a large number of densely annotated images for training to achieve good results,and the generalization ability for unknown categories is not strong.However,due to various factors such as instrument differences,there are big differences between private data sets of brain tumor images,and it is very difficult to obtain a large number of brain tumor image data with supervised information.At the same time,the brain tumor itself occupies a very small area in the brain tumor image,and the number of background pixels is much more than brain tumor pixels,and there is a problem of category imbalance during training.In view of these characteristics of brain tumor images,the following is the research content of this article:(1)Aiming at the multi-modal characteristics of brain tumor MR images,this paper adopts 2D slice fusion method to fuse the features of the four modalities including T1,T1 ce,T2,and FLAIR in brain tumor MR images to obtain an image containing four modal features,Make preliminary preparations for the subsequent segmentation task.(2)For pre-processed brain tumor MR images,this paper combines few-shot learning and deep learning theory,and proposes a new network structure PU-net(Prototype Network based on U-net),which is used for multi-modal MR images of brain tumors segmentation.Introducing the idea of few-shot learning,it can use limited supervision information to generalize the model,improve based on the prototype network in the metric learning method,and apply it to the segmentation task without introducing additional training parameters.(3)The feature extractor is optimized on the basis of U-net,which can fuse shallow features and deep features,and learn the distribution law of brain tumors and the precise details of brain tumors quickly;optimize the downsampling method and convolution method to reduce the information loss in the feature extraction process;using early fusion structure to extract ROI,thereby can avoid category imbalance problems,and can be used for subsequent feature extraction calculations.(4)On the basis of the proposed PU-net,an attention mechanism is added,that is,an attention gate is added to the feature extractor,and the PAU-net model is further proposed for brain tumors segmentation,and ROI is extracted through the attention gate.Combining it and late fusion instead of early fusion in the model,thereby further improving the model performance.Through experiments,it is concluded that the evaluation indexes of the segmentation of the PAU-net(Prototype Network based on U-net with Attention gate)method proposed in this paper on the Bra TS18 dataset surpass the latest few-shot segmentation algorithm,and can achieve good segmentation for few-shot brain tumor multimodal MR images effect.