无需训练数据，零样本矫正磁共振图像偏移不均。
Zero-shot Bias Correction: Efficient MR Image Inhomogeneity Reduction Without Any Data
- 设计轻量CNN，零样本直接优化修正图像偏移。
- 在多个数据集上优于现有无数据N4方法，效率与精度更优。
- 适合缺乏标注数据的医学图像处理场景，部署便捷。
近年来，深度神经网络在图像非均匀性校正方面表现优异。然而，当前的有监督或无监督方法需准备训练数据，数据收集成本高且耗时。本文提出一种新型零样本深度神经网络，无需预训练数据，也不依赖偏置场假设。所设计的轻量级卷积网络可实现高效的零样本自适应，通过迭代同质性精修策略，稳定收敛地校正偏移污染图像。在多个数据集上的大量对比实验表明，该方法在效率和准确率上均优于现有无数据N4方法。
原文摘要 · Abstract (English)
In recent years, deep neural networks for image inhomogeneity reduction have shown promising results. However, current methods with (un)supervised solutions require preparing a training dataset, which is expensive and laborious for data collection. In this work, we demonstrate a novel zero-shot deep neural networks, which requires no data for pre-training and dedicated assumption of the bias field. The designed light-weight CNN enables an efficient zero-shot adaptation for bias-corrupted image correction. Our method provides a novel solution to mitigate the biased corrupted image as iterative homogeneity refinement, which therefore ensures the considered issue can be solved easier with stable convergence of zero-shot optimization. Extensive comparison on different datasets show that the proposed method performs better than current data-free N4 methods in both efficiency and accuracy.
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