Brain Tumor Image Segmentation Based on Grouped Convolution

Abstract

The automatic segmentation of MRI multi-modal images of brain tumors is one of the important research contents of disease detection and analysis. Due to the heterogeneity of tumors, it is difficult to achieve efficient and accurate automatic segmentation of brain tumors. Traditional segmentation methods based on machine learning cannot handle complex scenes such as complex edges and overlapping categories. In clinical assisted diagnosis, it is of great significance to apply deep learning to two-dimensional natural image segmentation and three-dimensional medical image segmentation. In this paper, we propose a three-dimensional network model to achieve precise segmentation of brain tumors. The model adopts an encoder-decoder structure and replaces ordinary convolution with grouped convolution to reduce network parameters and improve network performance. The model improves the problem of information exchange between different groups through channel mixing. Experiments conducted on the BraTS (Brain Tumor Segmentation) 2018 challenge dataset prove that our network greatly reduces the computational cost while ensuring segmentation accuracy.

Hippocampus’s volume calculation on coronal slice’s for strengthening the diagnosis of Alzheimer’s

“…Hadi et al [18] proposed region-based segmentation of brain tumors using an active contour model. Wu et al [19] proposed a three-dimensional network model for brain tumor segmentation by adopting an encoder-decoder structure and replacing convolution with group convolution to improve network performance. This paper will discuss calculating the hippocampus's brain volume based on the coronal slice of MRI segmentation images based on time, the value of CDR, gender, and age.…”