Adaptive post-processing refines outputs from pre-trained glioma segmentation models, improving challenge metrics by 14.9% in one task and 0.9% in another.
arXiv preprint arXiv:2412.04094 (2024)
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A radiomic-guided subtyping and lesion-wise ensemble pipeline delivers segmentation performance comparable to top entries on diverse BraTS 2025 brain tumor datasets.
Ensembling inpainting models with median filtering, histogram matching, pixel averaging, and lightweight U-Net refinement yields more anatomically plausible and accurate inpainted MRI regions than individual baseline models.
citing papers explorer
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Improving Pre-trained Segmentation Models using Post-Processing
Adaptive post-processing refines outputs from pre-trained glioma segmentation models, improving challenge metrics by 14.9% in one task and 0.9% in another.
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Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-guided Subtyping and Lesion-Wise Model Ensemble
A radiomic-guided subtyping and lesion-wise ensemble pipeline delivers segmentation performance comparable to top entries on diverse BraTS 2025 brain tumor datasets.
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Post-Processing Methods for Improving Accuracy in MRI Inpainting
Ensembling inpainting models with median filtering, histogram matching, pixel averaging, and lightweight U-Net refinement yields more anatomically plausible and accurate inpainted MRI regions than individual baseline models.