Brain Tumor Segmentation and Survival Prediction using 3D Attention UNet

• Main Authors: Islam, Mobarakol, VS, Vibashan, Jose, V Jeya Maria, Wijethilake, Navodini, Utkarsh, Uppal, Ren, Hongliang
• Format: Journal Article
• Language: English
• Published: 02-04-2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2104.00985

In this work, we develop an attention convolutional neural network (CNN) to
segment brain tumors from Magnetic Resonance Images (MRI). Further, we predict
the survival rate using various machine learning methods. We adopt a 3D UNet
architecture and integrate channel and spatial attention with the decoder
network to perform segmentation. For survival prediction, we extract some novel
radiomic features based on geometry, location, the shape of the segmented tumor
and combine them with clinical information to estimate the survival duration
for each patient. We also perform extensive experiments to show the effect of
each feature for overall survival (OS) prediction. The experimental results
infer that radiomic features such as histogram, location, and shape of the
necrosis region and clinical features like age are the most critical parameters
to estimate the OS.