Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging
<|reference_start|>Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging: Segmenting brain tumors in multi-parametric magnetic resonance imaging enables performing quantitative analysis in support of clinical trials and personalized patient care. This analysis provides the potential to impact clinical decision-making processes, including diagnosis and prognosis. In 2023, the well-established Brain Tumor Segmentation (BraTS) challenge presented a substantial expansion with eight tasks and 4,500 brain tumor cases. In this paper, we present a deep learning-based ensemble strategy that is evaluated for newly included tumor cases in three tasks: pediatric brain tumors (PED), intracranial meningioma (MEN), and brain metastases (MET). In particular, we ensemble outputs from state-of-the-art nnU-Net and Swin UNETR models on a region-wise basis. Furthermore, we implemented a targeted post-processing strategy based on a cross-validated threshold search to improve the segmentation results for tumor sub-regions. The evaluation of our proposed method on unseen test cases for the three tasks resulted in lesion-wise Dice scores for PED: 0.653, 0.809, 0.826; MEN: 0.876, 0.867, 0.849; and MET: 0.555, 0.6, 0.58; for the enhancing tumor, tumor core, and whole tumor, respectively. Our method was ranked first for PED, third for MEN, and fourth for MET, respectively.<|reference_end|>
@article{capellán-martín2024model,
    title={Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance
  Imaging},
    author={Daniel Capell'an-Mart'in, Zhifan Jiang, Abhijeet Parida, Xinyang
  Liu, Van Lam, Hareem Nisar, Austin Tapp, Sarah Elsharkawi, Maria J.
  Ledesma-Carbayo, Syed Muhammad Anwar, Marius George Linguraru},
    journal={arXiv preprint arXiv:2409.08232},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.08232},
    primaryClass={eess.IV cs.CV}
}