# Post-Processing Methods for Improving Accuracy in MRI Inpainting

arXiv: https://arxiv.org/abs/2510.15282 · HTML: https://mlsanity.com/paper/2510.15282/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 29 references that are in the map.
- First posted on arXiv 17 Oct 2025.

## Builds on (most cited first, 13 of 13)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Analysis of the MICCAI Brain Tumor Segmentation -- Metastases (BraTS-METS) 2025 Lighthouse Challenge: Brain Metastasis Segmentation on Pre- and Post-treatment MRI](https://mlsanity.com/paper/2504.12527/) (2025)
- [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024)
- [Symmetric-Constrained Irregular Structure Inpainting for Brain MRI Registration with Tumor Pathology](https://mlsanity.com/paper/2101.06775/) (2021)
- [The Brain Tumor Segmentation (BraTS) Challenge: Local Synthesis of Healthy Brain Tissue via Inpainting](https://mlsanity.com/paper/2305.08992/) (2023)
- [BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis](https://mlsanity.com/paper/2506.13807/) (2025)
- [Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging](https://mlsanity.com/paper/2409.08232/) (2024)
- [Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data](https://mlsanity.com/paper/2412.04111/) (2024)
- [Longitudinal detection of new MS lesions using Deep Learning](https://mlsanity.com/paper/2206.08272/) (2022)
- [Advancing Brain Tumor Inpainting with Generative Models](https://mlsanity.com/paper/2402.01509/) (2024)
- [Brain Tumour Removing and Missing Modality Generation using 3D WDM](https://mlsanity.com/paper/2411.04630/) (2024)
- [Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation](https://mlsanity.com/paper/2412.04094/) (2024)
- [U-Net Based Healthy 3D Brain Tissue Inpainting](https://mlsanity.com/paper/2507.18126/) (2025)

## Built on by (most cited first, 1 of 1)

- [Sharpening the Ensemble: An SSIM-Aligned Residual Refiner for Brain-MRI Inpainting Post-Processing](https://mlsanity.com/paper/2609.03981/) (2026)

## Abstract (arXiv)

Magnetic Resonance Imaging (MRI) is the primary imaging modality used in the diagnosis, assessment, and treatment planning for brain pathologies. However, most automated MRI analysis tools, such as segmentation and registration pipelines, are optimized for healthy anatomies and often fail when confronted with large lesions such as tumors. To overcome this, image inpainting techniques aim to locally synthesize healthy brain tissues in tumor regions, enabling the reliable application of general-purpose tools. In this work, we systematically evaluate state-of-the-art inpainting models and observe a saturation in their standalone performance. In response, we introduce a methodology combining model ensembling with efficient post-processing strategies such as median filtering, histogram matching, and pixel averaging. Further anatomical refinement is achieved via a lightweight U-Net enhancement stage. Comprehensive evaluation demonstrates that our proposed pipeline improves the anatomical plausibility and visual fidelity of inpainted regions, yielding higher accuracy and more robust outcomes than individual baseline models. By combining established models with targeted post-processing, we achieve improved and more accessible inpainting outcomes, supporting broader clinical deployment and sustainable, resource-conscious research. Our 2025 BraTS inpainting docker is available at https://hub.docker.com/layers/aparida12/brats2025/inpt.