提出双网络结构，精准矫正磁共振图像强度不均问题
Structure and Smoothness Constrained Dual Networks for MR Bias Field Correction
- 设计双网络架构，引入结构与平滑性约束
- 在临床和模拟数据上均优于传统及深度学习方法
- 特别适合需要高精度结构保留的医学图像分割任务
磁共振成像对疾病诊断极具价值，但设备限制常导致图像存在显著的强度不均，影响定性和定量分析。近年来虽有多种无监督深度学习模型用于图像改善，但多聚焦于全局外观学习，忽视了图像结构和偏差场平滑性的约束，造成校正结果失真。本文提出新型结构与平滑性约束双网络（S2DNets），通过分段结构约束与偏差场平滑性建模，实现自监督偏差场校正，有效消除非均匀强度并保留更多结构细节。在临床与模拟磁共振数据集上的大量实验表明，该模型在视觉指标和下游图像分割任务中均优于现有方法。源代码已公开于https://github.com/LeongDong/S2DNets。
原文摘要 · Abstract (English)
MR imaging techniques are of great benefit to disease diagnosis. However, due to the limitation of MR devices, significant intensity inhomogeneity often exists in imaging results, which impedes both qualitative and quantitative medical analysis. Recently, several unsupervised deep learning-based models have been proposed for MR image improvement. However, these models merely concentrate on global appearance learning, and neglect constraints from image structures and smoothness of bias field, leading to distorted corrected results. In this paper, novel structure and smoothness constrained dual networks, named S2DNets, are proposed aiming to self-supervised bias field correction. S2DNets introduce piece-wise structural constraints and smoothness of bias field for network training to effectively remove non-uniform intensity and retain much more structural details. Extensive experiments executed on both clinical and simulated MR datasets show that the proposed model outperforms other conventional and deep learning-based models. In addition to comparison on visual metrics, downstream MR image segmentation tasks are also used to evaluate the impact of the proposed model. The source code is available at: https://github.com/LeongDong/S2DNets}{https://github.com/LeongDong/S2DNets.
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