Context versus Foreground-to-Background Ratio Trade-off

Overview

This work investigates a fundamental trade-off in 3D medical image segmentation: balancing global context against foreground-to-background ratio when using sliding window approaches due to GPU memory constraints. While larger context windows provide more global information, they also introduce severe class imbalance between background and foreground voxels.

Published at: Medical Imaging meets NeurIPS Workshop, NeurIPS 2022

Key Contributions

This work presents the first systematic study of the context versus foreground ratio trade-off in 3D medical segmentation, providing a novel analysis framework that has been missing from the literature. We conducted a comprehensive multi-architecture comparison by evaluating vanilla U-Net, Attention U-Net, and UNETR across this trade-off spectrum, revealing important architectural differences in how these models handle competing demands for global context and class balance.

Our investigation uncovered significant robustness insights, particularly that attention-based models are less robust to training and test distribution shifts in foreground ratios compared to traditional CNN architectures. This finding has important implications for clinical deployment where data distributions can vary significantly. Additionally, we established practical guidelines demonstrating that all architectures consistently prefer more global context over balanced foreground ratios, challenging conventional wisdom about class balance importance.

Methodology

Our approach combined controlled synthetic experiments using texture-based synthetic datasets specifically designed to isolate the trade-off effects, allowing us to systematically vary context and foreground ratio independently. We validated these findings through spleen segmentation experiments using data from the Medical Segmentation Decathlon, confirming that our synthetic insights translate to real medical imaging scenarios.

The study provided comprehensive architecture coverage by evaluating both traditional CNN approaches (vanilla U-Net, Attention U-Net) and newer Transformer-based methods (UNETR), ensuring our findings are applicable across the current landscape of 3D segmentation architectures.

Key Findings

Our experiments revealed that all three network types consistently favor larger context windows over balanced class ratios, suggesting that the information gained from additional spatial context outweighs the challenges introduced by increased class imbalance. This finding held across both synthetic and real medical imaging datasets.

We discovered important robustness differences between architectures, with UNETR and Attention U-Net showing greater sensitivity to foreground ratio variations compared to vanilla U-Net. This suggests that while attention mechanisms and transformer architectures may offer superior performance under ideal conditions, they may be more vulnerable to distribution shifts commonly encountered in clinical settings.

The results highlight critical performance trade-offs in 3D medical segmentation, where optimal performance requires careful balance between sufficient context and manageable class imbalance. These findings provide concrete guidance for practitioners designing segmentation pipelines under GPU memory constraints.

Citation

@inproceedings{kamath2022contextvsfbr,
    title={How do 3D image segmentation networks behave across the context versus foreground ratio trade-off?},
    author={Kamath, Amith and Suter, Yannick and You, Suhang and Mueller, Michael and Willmann, Jonas and Andratschke, Nicolaus and Reyes, Mauricio},
    booktitle={Medical Imaging Meets NeurIPS Workshop, Neural Information Processing Systems},
    year={2022},
    howpublished={\url{http://www.cse.cuhk.edu.hk/~qdou/public/medneurips2022/72.pdf}}
}