用简单方法提升术后胶质瘤分割准确率
Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models
- 用多模态MRI线性组合生成增强肿瘤特征输入
- 通过模型集成显著提升分割性能，Dice达0.78以上
- 适合需要高效可靠分割的临床研究者使用
分割是医学影像领域的重要任务，常作为后续分析的前提。但手术等治疗会显著增加病灶边界识别难度。为此，BraTS Post-Treatment 2024挑战赛发布了首个术后胶质瘤分割公开数据集，旨在推动自动化分割工具的发展。本文提出两种简洁有效的方法：首先，基于现有MRI序列的线性组合生成额外输入，突出强化肿瘤区域；其次，采用多种集成策略加权多个模型的输出。实验结果表明，这些方法相比基线模型显著提升分割性能，验证了简单策略在医学图像分割中的有效性。
原文摘要 · Abstract (English)
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.
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