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Improving Pre-trained Adult Glioma Segmentation Models using only Post-processing Techniques.

Abhijeet Parida^1,2, Daniel Capellán-Martín^1,2, Zhifan Jiang¹

¹Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.

Segmentation, Classification, and Synthesis for Brain Tumors and Traumatic Brain Injuries : MICCAI 2025 Challenges: Brats-Lighthouse 2025 and AIMS-TBI 2025, Held in Conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025

|May 18, 2026

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This summary is machine-generated.

Adaptive post-processing refines brain tumor segmentation from large models, improving accuracy for glioma challenges. This sustainable approach enhances clinical alignment and computational fairness in AI research.

Area of Science:

Neuro-oncology
Medical imaging analysis
Artificial intelligence in medicine

Background:

Gliomas are aggressive adult brain tumors with poor survival rates.
Accurate multiparametric MRI segmentation is vital for glioma treatment and monitoring.
Current deep learning models struggle with generalization and introduce segmentation errors.

Purpose of the Study:

To develop adaptive post-processing techniques for refining glioma segmentation.
To address limitations of large-scale pre-trained models in brain tumor segmentation.
To promote computationally fair and sustainable AI strategies in medical imaging.

Main Methods:

Proposed adaptive post-processing methods to correct segmentation errors.
Applied techniques to refine segmentations from pre-trained models.

Keywords:

BraTS Challenge Brain MRI Glioma segmentation Medical image analysis Resource-aware AI

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Evaluated performance on BraTS 2025 segmentation challenge tasks.

Main Results:

Achieved a 14.9% improvement in the ranking metric for the sub-Saharan Africa challenge.
Secured a 0.9% improvement for the adult glioma challenge.
Demonstrated effectiveness in refining segmentations from generalized models.

Conclusions:

Adaptive post-processing offers an efficient alternative to complex model architectures.
This approach enhances precision, computational fairness, and sustainability in brain tumor segmentation.
Shifts focus towards clinically aligned, post-processing strategies for AI in neuro-oncology.