Research On Brain Tumor Segmentation Based On Global Aggregation Cascade Network

In recent years,with the increase of people's intake of nitroso compounds in processed foods and the influence of some environmental factors,brain tumors have become one of the common tumors that threaten people's lives.According to the latest research results,my country is already in the forefront of the world in terms of the number of brain tumor patients and the number of patients who have lost their lives due to brain tumors.Brain tumors are difficult to diagnose and treat.Magnetic resonance imaging(MRI for short)is based on the energy that has been released,the differential attenuation of a specific object under its own diversified structure conditions,and the use of a gradient magnetic field to detect a certain number of electromagnetic waves that have been emitted,and the object can be obtained Based on the actual position and specific category of the nucleus,the structural image corresponding to the structure of the object can be drawn,which is conducive to scientific,effective and accurate detection of brain tumors,and a more in-depth and detailed analysis can be carried out.After viewing the MRI images in real time,the doctor can make a rough judgment on whether the subject has a brain tumor and the specific severity of the brain tumor.However,MRI images also have some drawbacks,such as the MRI images generated by the excessive number of patients every day There will be a large number,and there are many influencing factors such as artifacts and noise in the image that will interfere with the diagnosis.It takes a lot of time and energy to rely on the doctor's experience to make a diagnosis with the naked eye,and it will happen due to insufficient experience or energy consumption.Risks of misjudgment caused by non-concentration.In order to overcome the above problems,medical image processing,an important branch of deep learning,has made considerable progress.The purpose of medical image processing is to use computers to process MRI images and achieve better results.Medical image processing mainly has two directions: It includes image classification.In addition,it also involves image segmentation.The first direction is helpful to determine the specific type of brain tumor,while the second direction is helpful to determine the basic shape and prescribedlocation of the tumor.Some characteristics of brain gliomas will bring some difficulties.For example,the unique high heterogeneity of brain tumors can show significant non-uniformity in the image.Therefore,for the medical image processing process,if It is undoubtedly extremely difficult to segment brain tumors scientifically and accurately.In the current period,one of the effective methods that have been used in this area is deep learning,and scholars have achieved some meaningful results in this area.However,the current medical image segmentation is mainly based on a large number of labeled data.However,for the current brain tumor data,the number has not yet reached this standard,and the data is slightly complicated.Not only that,because the brain There is a significant high degree of heterogeneity in tumors,so the intra-class differences between sub-regions are also more significant.At the same time,there are also certain inter-class differences in certain regions.In this paper,based on deep learning,combined with the popular computer vision technology nowadays,a deeper and more detailed exploration of MRI technology is carried out.The detailed work content is as follows:1.The Bra TS 2019 MRI image classification and segmentation data set was classified and preprocessed,so that the data set was amplified and adjusted to the size required by the model.2.All brain tumor MRI images have been corrected by N4 ITK,hoping to minimize the deviation caused by the differences of various scanners,institutions and protocols.3.Inspired by the two-level cascade U-Net,the Global Aggregation Block(global aggregation block)is introduced into its basic architecture to propose a global aggregated two-stage cascade network.4.We use the groung truth brain tumor lesions of a group of patients to generate heat maps of different types of lesions.These heat maps are used to create a volume of interest(VOI)map,which contains prior information about brain tumor lesions.Then the VOI mapping is integrated with the multi-modal MR image and used as the input of the network for brain tumor segmentation.