Medical Image Analysis Based On Radiomics And Deep Learning

With the rapid development of medical imaging technology,medical imaging plays an increasingly important role in the medical industry.It is no longer an auxiliary tool for doctors and has gradually become a core tool for medical diagnosis.Especially in the field of cancer diagnosis and prognosis,due to the serious genetic mutations in the internal tissues of cancer,there is serious spatial and temporal heterogeneity inside the cancer.This spatial and temporal heterogeneity severely hampers cancer diagnosis and prognosis based on invasive biopsy.However,medical images can observe the entire tumor mass in the form of images,and medical images can acquire tumor phenotypes in a non-invasive manner.It thus can gain internal genetic traits.Therefore,radiomics,a discipline that can effectively quantify medical images,has developed rapidly.radiomics can quantify medical images into a large number of image features,and characterize the phenotype of the tumor through feature selection,machine learning,statistical analysis,etc.and then find the connection with the genetic characteristics.At the same time,deep learning has rapidly developed in the field of computer vision,and has become the most efficient method in the field of computer vision.However,due to the amount of data,deep learning cannot directly apply computer vision effective methods to medical imaging.Therefore,how to apply deep learning to medical imaging field has become an important research issue.In view of the above two points,the purpose of this paper is to explore the application methods of radiomics and deep learning in medical imaging.Firstly,based on the method of radiomics,this paper proposes a method that can be automatically used for tumor staging evaluation,and fully summarizes The process and challenges of radiomics.After that,this paper explores the application of deep learning in the field of medical image segmentation,fully explores the existing deep learning methods for medical image segmentation,and creatively proposes a network suitable for segmentation of renal cancer tumor blocks and improves The effect of tumor block segmentation.Finally,this paper explores the possibility of combining radiomics and deep learning.Through the deep learning method,it effectively breaks through the performance bottleneck of radiomics,which greatly improves the performance of Survival prediction in glioma patients.In the process of exploring the application of radiomics in medical imaging,this paper first explores the application of radiomics in tumor staging evaluation,and designs a model based on radiomics for the risk rating of glioma.The model can automate the staging of tumors,and all processes are based on radiomics.First,based on the summary of the current radiomics method,an efficient library of radiomics features is customized,and then this ensemble is used.The feature library segmented the sub-regions of the tumor,and then used the segmentation results and radiomics feature library to model,and evaluated the performance of the two feature selection methods and the three classification methods.The final model has good performance in glioma risk ratings.This paper also explores the application of deep learning in medical imaging.For the problem of medical image segmentation,this paper explores the segmentation of renal cancer tumor mass.Firstly,the deep learning model commonly used in medical image segmentation is explored,and a network suitable for segmentation of renal cancer tumor blocks is designed for the special field of renal cancer tumor block segmentation.The principle and high performance of network design are proved through a series of experiments.And a series of experiments have been used to explore the network's suitable loss function.After matching the appropriate loss function,the network greatly improves the performance of renal cancer tumor block segmentation.Subsequently,this paper attempts to combine the advantages of these two methods.In a small sample of glioma survival prediction problems,the use of deep transfer learning methods can effectively help radiomics breakthrough performance bottlenecks,the characteristics of convolutional neural networks Learning ability helps radiomics expand the feature library.Based on radiomics and transfer learning methods,this paper has achieved good performance in predicting the survival of glioma.This paper found an radiomics signature with excellent survival prediction ability.And combined with the traditional survival risk factors to obtain the best survival prediction performance,according to this,this paper designs a nomogram of survival prediction of glioma patients,which is helpful for the formulation of prognosis treatment plans for patients.