用模糊逻辑增强U-Net，提升脑部MRI图像分割精度
A Novel Framework using Intuitionistic Fuzzy Logic with U-Net and U-Net++ Architecture: A case Study of MRI Bain Image Segmentation
- 将直觉模糊逻辑融入U-Net与U-Net++，处理图像不确定性
- 在IBSR和OASIS数据集上，Dice系数等指标显著提升
- 适合需要高精度医学图像分割的研究者使用
从磁共振成像（MRI）扫描中准确分割脑部图像，在脑图分析和神经疾病诊断中至关重要。尽管深度学习模型如U-Net和U-Net++广泛用于图像分割，但难以应对图像中的不确定性。为解决这一问题，本文将直觉模糊逻辑引入U-Net和U-Net++，提出新型框架IFS U-Net和IFS U-Net++。这些模型以直觉模糊表示输入数据，有效管理由模糊性和不精确数据引发的不确定性，显著缓解因部分容积效应和边界模糊带来的组织歧义。通过在两个公开的MRI脑部数据集——互联网脑分割资源库（IBSR）和开放获取影像研究系列（OASIS）上进行实验，采用准确率、Dice系数和交并比（IoU）进行定量评估。结果表明，所提架构在多个指标上均持续提升分割性能，有效应对不确定性。
原文摘要 · Abstract (English)
Accurate segmentation of brain images from magnetic resonance imaging (MRI) scans plays a pivotal role in brain image analysis and the diagnosis of neurological disorders. Deep learning algorithms, particularly U-Net and U-Net++, are widely used for image segmentation. However, it finds difficult to deal with uncertainty in images. To address this challenge, this work integrates intuitionistic fuzzy logic into U-Net and U-Net++, propose a novel framework, named as IFS U-Net and IFS U-Net++. These models accept input data in an intuitionistic fuzzy representation to manage uncertainty arising from vague ness and imprecise data. This approach effectively handles tissue ambiguity caused by the partial volume effect and boundary uncertainties. To evaluate the effectiveness of IFS U-Net and IFS U-Net++, experiments are conducted on two publicly available MRI brain datasets: the Internet Brain Segmentation Repository (IBSR) and the Open Access Series of Imaging Studies (OASIS). Segmentation performance is quantitatively assessed using Accuracy, Dice Coefficient, and Intersection over Union (IoU). The results demonstrate that the proposed architectures consistently improve segmentation performance by effectively addressing uncertainty
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