Using U-Net Network for Efficient Brain Tumor Segmentation in MRI Images

TL;DR

This paper introduces a lightweight U-Net architecture for real-time brain tumor segmentation in MRI images, achieving high accuracy with less data and computational resources, and utilizing 2D perspectives instead of 3D volumes.

Contribution

A novel lightweight U-Net model that enables efficient, real-time brain tumor segmentation without extensive data augmentation or large datasets.

Findings

Achieves 89% mean IoU on BITE dataset

Outperforms standard benchmark algorithms

Uses three perspective planes for simplified segmentation

Abstract

Magnetic Resonance Imaging (MRI) is the most commonly used non-intrusive technique for medical image acquisition. Brain tumor segmentation is the process of algorithmically identifying tumors in brain MRI scans. While many approaches have been proposed in the literature for brain tumor segmentation, this paper proposes a lightweight implementation of U-Net. Apart from providing real-time segmentation of MRI scans, the proposed architecture does not need large amount of data to train the proposed lightweight U-Net. Moreover, no additional data augmentation step is required. The lightweight U-Net shows very promising results on BITE dataset and it achieves a mean intersection-over-union (IoU) of 89% while outperforming the standard benchmark algorithms. Additionally, this work demonstrates an effective use of the three perspective planes, instead of the original three-dimensional volumetric images, for simplified brain tumor segmentation.