对比深度学习与传统方法，发现nnU-Net结合预处理在脑组织分割中表现最佳。
Comparative Study of Probabilistic Atlas and Deep Learning Approaches for Automatic Brain Tissue Segmentation from MRI Using N4 Bias Field Correction and Anisotropic Diffusion Pre-processing Techniques
- 用N4和各向异性扩散预处理图像，再比较概率图谱与深度学习模型
- 3D nnU-Net Dice系数达0.937，2D模型在距离和体积误差上最优
- 适合医学影像分析者参考，代码开源便于复现
从磁共振成像（MRI）中自动分割脑组织对精准诊断至关重要。尽管分割技术不断进步，但传统统计方法与现代深度学习方法在结合N4偏置场校正和各向异性扩散预处理下的系统性对比仍较少。本研究在IBSR18数据集上对比了概率图谱ATLAS、U-Net、nnU-Net和LinkNet等模型，用于分割白质（WM）、灰质（GM）和脑脊液（CSF）。结果表明，3D nnU-Net取得最高平均Dice系数（0.937 ± 0.012），2D nnU-Net在五个未见测试样本上表现最优：平均豪斯多夫距离为5.005 ± 0.343 mm，平均绝对体积差异为3.695 ± 2.931 mm。研究证实nnU-Net在结合预处理技术下具有显著优势。代码已开源。
原文摘要 · Abstract (English)
Automatic brain tissue segmentation from Magnetic Resonance Imaging (MRI) images is vital for accurate diagnosis and further analysis in medical imaging. Despite advancements in segmentation techniques, a comprehensive comparison between traditional statistical methods and modern deep learning approaches using pre-processing techniques like N4 Bias Field Correction and Anisotropic Diffusion remains underexplored. This study provides a comparative analysis of various segmentation models, including Probabilistic ATLAS, U-Net, nnU-Net, and LinkNet, enhanced with these pre-processing techniques to segment brain tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF)) on the Internet Brain Segmentation Repository (IBSR18) dataset. Our results demonstrate that the 3D nnU-Net model outperforms others, achieving the highest mean Dice Coefficient score (0.937 +- 0.012), while the 2D nnU-Net model recorded the lowest mean Hausdorff Distance (5.005 +- 0.343 mm) and the lowest mean Absolute Volumetric Difference (3.695 +- 2.931 mm) across five unseen test samples. The findings highlight the superiority of nnU-Net models in brain tissue segmentation, particularly when combined with N4 Bias Field Correction and Anisotropic Diffusion pre-processing techniques. Our implemented code can be accessed via GitHub.
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