Segmentation of Brain Tumor Tissues in HGG and LGG MR Images Using 3D U-net Convolutional Neural Network

Literature Survey

This Section highlights the active researchers working in the area of brain tumor segmentation. The research, development, and scientific publications in the area of computerized brain tumor segmentation have grown faster in the last few decades, and this shows that the brain tumor segmentation research is still a work in progress but also signifies the need for computerized brain tumor segmentation methods in large clinical institutions.

Segmentation of brain tumor methods is grouped into two types: Generative model and the other is based on the Discriminative model. The Generative models employ detailed prior knowledge about the spatial distribution and appearance of cells. These models display good generalization to unseen images and has represented promising results for segmentation of brain tumors. The prior knowledge encoding of a brain tumor is a tedious task. Based on the expected shape or image signal of non-tumorous brain tissues, brain tumors are modelled as outliers. The tumor specific “bio-markers” are employed to extract the spatial information. And, also models of tumor growth are employed to infer the probable tumor structures. The Generative models depend on image registration for properly aligning the images and spatial priors; which is a hard task in presence of big size brain tumor structures.