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.

本研究针对术后胶质瘤分割中常见的困难，提出了两种简单的方法以提升深度学习模型的分割性能。通过引入基于线性组合的额外MRI序列输入和多种集成方法，本研究显著改善了分割效果，强调了这些简单方法在医学影像分割任务中的有效性和潜在影响。

使用简单方法有效分割治疗后胶质瘤：人工序列生成与集成模型