Deep Learning-Based Approach for Automatic 2D and 3D MRI Segmentation of Gliomas

TL;DR

This paper presents a novel deep learning approach combining 2D and 3D convolutional neural networks based on UNET, Inception, and ResNet architectures for automatic glioma segmentation in MRI images, achieving high accuracy and dice scores.

Contribution

It introduces an optimized model balancing 2D and 3D convolutional efficiencies for improved glioma segmentation, validated on multiple BraTS datasets.

Findings

ResNet achieved 98.91% accuracy in 3D segmentation

Dice score of 0.8312 for 2D segmentation

Dice score of 0.9888 for 3D segmentation

Abstract

Brain tumor diagnosis is a challenging task for clinicians in the modern world. Among the major reasons for cancer-related death is the brain tumor. Gliomas, a category of central nervous system (CNS) tumors, encompass diverse subregions. For accurate diagnosis of brain tumors, precise segmentation of brain images and quantitative analysis are required. A fully automatic approach to glioma segmentation is required because the manual segmentation process is laborious, prone to mistakes, as well as time-consuming. Modern techniques for segmenting gliomas are based on fully convolutional neural networks (FCNs), which can either use two-dimensional (2D) or three-dimensional (3D) convolutions. Nevertheless, 3D convolutions suffer from computational costs and memory demand, while 2D convolutions cannot fully utilize the spatial insights of volumetric clinical imaging data. To obtain an optimal solution, it is vital to balance the computational efficiency of 2D convolutions along with the spatial accuracy of 3D convolutions. This balance can potentially be realized by developing an advanced model to overcome these challenges. The 2D and 3D models implemented here are based on UNET architecture, Inception, and ResNet models. The research work has been implemented on the BraTS 2018, 2019, and 2020 datasets. The best performer of all the models' evaluations metrics for proposed methodologies offer superior potential in terms of the effective segmentation of gliomas. The ResNet model has resulted in 98.91% accuracy for 3D segmentation and 99.77 for 2D segmentations. The dice scores for 2D and 3D segmentations are 0.8312 and 0.9888, respectively. This model can be applied to various other medical applications with fine-tuning, thereby aiding clinicians in brain tumor analysis and improving the diagnosis process effectively.