用模糊逻辑增强UNet，更好处理脑部图像的模糊边界问题。
An Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation
- 引入直觉模糊逻辑，同时考虑隶属度、非隶属度和犹豫度
- 在IBSR数据集上，Dice系数与IoU均优于传统UNet
- 适合处理因部分体积效应导致的医学图像不确定性
准确分割MRI脑部图像对医学图像计算和神经系统疾病诊断至关重要。深度学习中，卷积神经网络（CNN）尤其是UNet被广泛用于医学图像分割。然而，由于脑部图像存在部分体积效应，难以有效处理不确定性。为此，本文提出一种增强框架IF-UNet，将直觉模糊逻辑融入UNet。该模型以隶属度、非隶属度和犹豫度处理输入数据，更有效地应对因部分体积效应引起的组织模糊和边界不确定。在互联网脑部分割资源库（IBSR）数据集上评估，采用准确率、Dice系数和交并比（IoU）进行性能分析。实验结果表明，IF-UNet在处理脑部图像不确定性方面显著提升了分割质量。
原文摘要 · Abstract (English)
Accurate segmentation of MRI brain images is essential for image analysis, diagnosis of neuro-logical disorders and medical image computing. In the deep learning approach, the convolutional neural networks (CNNs), especially UNet, are widely applied in medical image segmentation. However, it is difficult to deal with uncertainty due to the partial volume effect in brain images. To overcome this limitation, we propose an enhanced framework, named UNet with intuitionistic fuzzy logic (IF-UNet), which incorporates intuitionistic fuzzy logic into UNet. The model processes input data in terms of membership, nonmembership, and hesitation degrees, allowing it to better address tissue ambiguity resulting from partial volume effects and boundary uncertainties. The proposed architecture is evaluated on the Internet Brain Segmentation Repository (IBSR) dataset, and its performance is computed using accuracy, Dice coefficient, and intersection over union (IoU). Experimental results confirm that IF-UNet improves segmentation quality with handling uncertainty in brain images.
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