Memory efficient brain tumor segmentation using an autoencoder-regularized U-Net

TL;DR

This paper introduces a memory-efficient 3D CNN with autoencoder regularization for brain tumor segmentation, achieving accurate results on MRI data using affordable hardware.

Contribution

The authors develop a novel autoencoder-regularized U-Net that is optimized for low-memory hardware, enabling effective brain tumor segmentation on standard GPUs.

Findings

Successfully segmented tumors into three subregions with high accuracy.

Achieved good performance using only a single NVIDIA GTX1060 GPU.

Enhanced segmentation performance through data augmentation and preprocessing.

Abstract

Early diagnosis and accurate segmentation of brain tumors are imperative for successful treatment. Unfortunately, manual segmentation is time consuming, costly and despite extensive human expertise often inaccurate. Here, we present an MRI-based tumor segmentation framework using an autoencoder-regularized 3D-convolutional neural network. We trained the model on manually segmented structural T1, T1ce, T2, and Flair MRI images of 335 patients with tumors of variable severity, size and location. We then tested the model using independent data of 125 patients and successfully segmented brain tumors into three subregions: the tumor core (TC), the enhancing tumor (ET) and the whole tumor (WT). We also explored several data augmentations and preprocessing steps to improve segmentation performance. Importantly, our model was implemented on a single NVIDIA GTX1060 graphics unit and hence optimizes tumor segmentation for widely affordable hardware. In sum, we present a memory-efficient and affordable solution to tumor segmentation to support the accurate diagnostics of oncological brain pathologies.