Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models

TL;DR

This paper introduces simple yet effective methods for post-treatment glioma segmentation in MRI data, utilizing artificial sequence generation and ensemble models to improve deep learning performance.

Contribution

It proposes straightforward techniques—sequence combination and model ensembling—that enhance segmentation accuracy in post-treatment glioma MRI analysis.

Findings

Significant improvement over baseline models

Artificial sequence generation highlights tumor regions

Ensemble methods effectively combine model outputs

Abstract

Segmentation is a crucial task in the medical imaging field and is often an important primary step or even a prerequisite to the analysis of medical volumes. Yet treatments such as surgery complicate the accurate delineation of regions of interest. The BraTS Post-Treatment 2024 Challenge published the first public dataset for post-surgery glioma segmentation and addresses the aforementioned issue by fostering the development of automated segmentation tools for glioma in MRI data. In this effort, we propose two straightforward approaches to enhance the segmentation performances of deep learning-based methodologies. First, we incorporate an additional input based on a simple linear combination of the available MRI sequences input, which highlights enhancing tumors. Second, we employ various ensembling methods to weigh the contribution of a battery of models. Our results demonstrate that these approaches significantly improve segmentation performance compared to baseline models, underscoring the effectiveness of these simple approaches in improving medical image segmentation tasks.