Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models
<|reference_start|>Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models: 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.<|reference_end|>
@article{kim2024effective,
    title={Effective Segmentation of Post-Treatment Gliomas Using Simple
  Approaches: Artificial Sequence Generation and Ensemble Models},
    author={Heejong Kim, Leo Milecki, Mina C Moghadam, Fengbei Liu, Minh Nguyen,
  Eric Qiu, Abhishek Thanki, and Mert R Sabuncu},
    journal={arXiv preprint arXiv:2409.08143},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08143},
    primaryClass={eess.IV cs.CV}
}