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.

翻译：磁共振成像（MRI）脑图像的精确分割对于图像分析、神经系统疾病诊断及医学图像计算至关重要。在深度学习方法中，卷积神经网络（CNN），尤其是UNet，已广泛应用于医学图像分割。然而，由于脑图像中的部分容积效应，处理不确定性因素具有挑战性。为克服这一局限，我们提出一种增强框架——融合直觉模糊逻辑的UNet（IF-UNet），将直觉模糊逻辑整合到UNet架构中。该模型通过隶属度、非隶属度和犹豫度处理输入数据，使其能更好地应对部分容积效应和边界不确定性导致的组织模糊问题。所提架构在互联网脑分割数据库（IBSR）数据集上进行评估，并采用准确率、Dice系数和交并比（IoU）量化其性能。实验结果表明，IF-UNet通过有效处理脑图像中的不确定性，显著提升了分割质量。