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

TL;DR

This paper presents a novel data-efficient segmentation framework for meningiomas that combines implicit spatiotemporal mixing and Sim2Real semantic injection, significantly improving model robustness with limited annotated data.

Contribution

It introduces a dual-augmentation framework utilizing INR-based deformation mixing and high-fidelity lesion simulation to enhance data efficiency in medical image segmentation.

Findings

Improved segmentation accuracy with limited data.

Enhanced robustness of models like nnU-Net and U-Mamba.

Significant gains in data efficiency and structural diversity exploration.

Abstract

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.