Glioma Segmentation with Cascaded Unet

TL;DR

This paper introduces a cascaded 3D UNet-based deep learning approach for brain tumor segmentation in MRI, improving accuracy by leveraging multi-scale context and data augmentation, and demonstrates its effectiveness on the BraTS 2018 dataset.

Contribution

It proposes a novel cascaded neural network architecture with modifications for multimodal MRI segmentation, enhancing segmentation quality through multi-scale context integration.

Findings

Achieved improved segmentation accuracy on BraTS 2018 dataset.

Enhanced model robustness with data augmentation strategies.

Demonstrated the effectiveness of cascaded architecture for brain tumor segmentation.

Abstract

MRI analysis takes central position in brain tumor diagnosis and treatment, thus it's precise evaluation is crucially important. However, it's 3D nature imposes several challenges, so the analysis is often performed on 2D projections that reduces the complexity, but increases bias. On the other hand, time consuming 3D evaluation, like, segmentation, is able to provide precise estimation of a number of valuable spatial characteristics, giving us understanding about the course of the disease.\newline Recent studies, focusing on the segmentation task, report superior performance of Deep Learning methods compared to classical computer vision algorithms. But still, it remains a challenging problem. In this paper we present deep cascaded approach for automatic brain tumor segmentation. Similar to recent methods for object detection, our implementation is based on neural networks; we propose modifications to the 3D UNet architecture and augmentation strategy to efficiently handle multimodal MRI input, besides this we introduce approach to enhance segmentation quality with context obtained from models of the same topology operating on downscaled data. We evaluate presented approach on BraTS 2018 dataset and discuss results.