Segmentation of Low-Grade Gliomas using U-Net VGG16 with Transfer Learning
Dwilaksana Abdullah Rasyid, Guan Huang, Nur Iriawan · 2021
Around 2000 cases of gliomas are diagnosed every year in the US, representing 23.41 percent of all primary brain tumors. World Health Organization (WHO) grade II gliomas or Low-Grade Gliomas (LGG) are slow-growing brain tumors. LGG is a fatal disease of young adults (between 35 and 44 years of age). LGG can transform into High-Grade Gliomas (HGG) or WHO grades III and IV occurred in most patients and ultimately leading to death. General treatment for LGG patients is surgical resection, radiotherapy, and chemotherapy. Fluid-Attenuated Inversion Recovery (FLAIR) imaging is needed to determine the tumor location before doing surgical resection. We propose a combined architectural innovation of U-Net and VGG16 with transfer learning as a hybrid model for tumor segmentation. Employing the preoperative FLAIR imaging data of 110 patients with LGG from the Cancer Genome Atlas, this deep learning algorithm achieves a high result with the Dice Similarity Coefficient of 99% and the Area Under Curve (AUC) of 98%, better than the previous approach done by Buda, et al.