Brain Tumor Segmentation in Multimodal MRI Using U-Net Layered Structure

The brain tumour is the mass where some tissues become old or damaged,but they do not die or not leave their space.Mainly brain tumour masses occur due to malignant masses.These tissues must die so that new tissues are allowed to be born and take their place.Tumour segmentation is a complex and time-taking problem due to the tumour’s size,shape,and appearance variation.Manually finding such masses in the brain by analyzing Magnetic Resonance Images(MRI)is a crucial task for experts and radiologists.Radiologists could not work for large volume images simultaneously,and many errors occurred due to overwhelming image analysis.The main objective of this research study is the segmentation of tumors in brain MRI images with the help of digital image processing and deep learning approaches.This research study proposed an automatic model for tumor segmentation in MRI images.The proposed model has a few significant steps,which first apply the pre-processing method for the whole dataset to convert Neuroimaging Informatics Technology Initiative(NIFTI)volumes into the 3D NumPy array.In the second step,the proposed model adopts U-Net deep learning segmentation algorithm with an improved layered structure and sets the updated parameters.In the third step,the proposed model uses state-of-the-art Medical Image Computing and Computer-Assisted Intervention(MICCAI)BRATS 2018 dataset withMRI modalities such as T1,T1Gd,T2,and Fluidattenuated inversion recovery(FLAIR).Tumour types in MRI images are classified according to the tumour masses.Labelling of these masses carried by state-of-the-art approaches such that the first is enhancing tumour(label 4),edema(label 2),necrotic and non-enhancing tumour core(label 1),and the remaining region is label 0 such that edema(whole tumour),necrosis and active.The proposed model is evaluated and gets the Dice Coefficient(DSC)value for High-grade glioma(HGG)volumes for their test set-a,test set-b,and test set-c 0.9795, 0.9855 and 0.9793, respectively. DSC value for the Low-gradeglioma (LGG) volumes for the test set is 0.9950, which shows the proposedmodel has achieved significant results in segmenting the tumour in MRI usingdeep learning approaches. The proposed model is fully automatic that canimplement in clinics where human experts consumemaximumtime to identifythe tumorous region of the brain MRI. The proposed model can help in a wayit can proceed rapidly by treating the tumor segmentation in MRI.

Determination of genetic relationships between evergreen azalea cultivars in China using AFLP markers

Evergreen azaleas are among the most important ornamental shrubs in China.Today,there are probably over 300 cultivars preserved in different nurseries,but with little information available on the cultivar itself or relationships between cultivars.Amplified fragment length polymorphism(AFLP) markers were employed to determine the genetic relationships between evergreen azalea cultivars in China.One hundred and thirty genotypes collected from gardens and nurseries,including cultivars classified in the groups East,West,Hairy,and Summer,unknown cultivars,and close species,were analyzed using three primer pairs.A total of 408 polymorphic fragments were generated by AFLP reactions with an average of 136 fragments per primer pair.The average values of expected heterozygosity and Shannon's information index were 0.3395 and 0.5153,respectively.Genetic similarities were generated based on Dice coefficients,used to construct a neighbor joining tree,and bootstrapped for 100 replicates in Treecon V1.3b.Principal coordinate analysis(PCO) was performed based on Dice distances using NTSYS-pc software.The AFLP technique was useful for analyzing genetic diversity in evergreen azaleas.Cluster analysis revealed that cultivars in the West and Summer groups were quite distinct from other groups in the four-group classification system and that the East and Hairy groups should be redefined.