# Data-Efficient Meningioma Segmentation via Implicit Spatiotemporal Mixing and Sim2Real Semantic Injection

arXiv: https://arxiv.org/abs/2601.17031 · HTML: https://mlsanity.com/paper/2601.17031/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 22 references that are in the map.
- First posted on arXiv 19 Jan 2026.

## Builds on (most cited first, 9 of 9)

- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Implicit Neural Representations with Periodic Activation Functions](https://mlsanity.com/paper/2006.09661/) (2020)
- [VoxelMorph: A Learning Framework for Deformable Medical Image Registration](https://mlsanity.com/paper/1809.05231/) (2018)
- [Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation](https://mlsanity.com/paper/2012.07177/) (2020)
- [Domain Adaptation for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2102.09508/) (2021)
- [Where Do We Stand with Implicit Neural Representations? A Technical and Performance Survey](https://mlsanity.com/paper/2411.03688/) (2024)
- [CarveMix: A Simple Data Augmentation Method for Brain Lesion Segmentation](https://mlsanity.com/paper/2108.06883/) (2021)
- [Domain Shift in Computer Vision models for MRI data analysis: An Overview](https://mlsanity.com/paper/2010.07222/) (2020)

## Abstract (arXiv)

The performance of medical image segmentation is increasingly defined by the efficiency of data utilization rather than merely the volume of raw data. Accurate segmentation, particularly for complex pathologies like meningiomas, demands that models fully exploit the latent information within limited high-quality annotations. To maximize the value of existing datasets, we propose a novel dual-augmentation framework that synergistically integrates spatial manifold expansion and semantic object injection. Specifically, we leverage Implicit Neural Representations (INR) to model continuous velocity fields. Unlike previous methods, we perform linear mixing on the integrated deformation fields, enabling the efficient generation of anatomically plausible variations by interpolating within the deformation space. This approach allows for the extensive exploration of structural diversity from a small set of anchors. Furthermore, we introduce a Sim2Real lesion injection module. This module constructs a high-fidelity simulation domain by transplanting lesion textures into healthy anatomical backgrounds, effectively bridging the gap between synthetic augmentation and real-world pathology. Comprehensive experiments on a hybrid dataset demonstrate that our framework significantly enhances the data efficiency and robustness of state-of-the-art models, including nnU-Net and U-Mamba, offering a potent strategy for high-performance medical image analysis with limited annotation budgets.