Brain Tumor Segmentation in MRI Images Using UNet based 3D CNN

Abstract

Deep Learning is quickly reaching into a wide variety of fields. It has found useful applications in Medical Image Analysis for various time and effort consuming tasks like fractionalization of brain tumor cells in Magnetic Resonance Imaging (MRI) scans. Large amount of time and effort is required for the segmentation process by doctors and radiologists due to the high quantity of data produced by scan centers. Fatal brain tumors like Glioblastoma need to be detected and diagnosed as quickly as possible. Therefore, Automatic methods of segmentation are required for faster and accurate detection, and analysis of fatal brain tumors. Several advances in image segmentation have been proposed the practice of two-dimensional Convolutional Neural Networks (CNN), but recent publications featured CNN applications using 3D kernels on 3D MRI images. Here we present an exploratory analysis of CNN based methods using 3D filters for segmentation of HighGrade Glioma (HGG) and Lower Grade Glioma (LGG) tumors in MRI images. Framework parameters like image resolution, batch size, and normalization techniques were optimized to gain better accuracy. The BraTS 2019 dataset was used for training provided by the University of Pennsylvania and MICCAI. The model was successfully trained on 250 patients having HGG for 100 epochs. A loss graph has shown the decrease of the training and validation loss over time. Dice coefficient (DC) parameter has been taken to evaluate the resemblance of the prediction with ground truth. This model has achieved mean dice score of 0.84 and 0.65 for WholeTumor and Tumor Core respectively.