arXiv 2602.042272026-02-04cs.CV

直觉模糊逻辑驱动的UNet架构：应用于脑图像分割

An Intuitionistic Fuzzy Logic Driven UNet architecture: Application to Brain Image segmentation

Department of Mathematics, Bareilly College, Bareilly (MJP Rohilkhand University), Uttar Pradesh, India（印度乌塔兰普尔邦巴雷利学院数学系）；Division of Newborn Medicine, Fetal Neonatal Neuroimaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02115, USA（美国哈佛医学院波士顿儿童医院新生儿医学部）；Department of Pediatrics, Harvard Medical School, Boston, MA, USA（美国哈佛医学院儿科系）；CSIR–Central Electronics Engineering Research Institute, Pilani 333031, Rajasthan, India（印度拉贾斯坦邦比拉尼中央电子工程研究机构）；Academy of Scientific and Innovative Research (AcSIR), Ghaziabad 201002, India（印度德里阿齐亚德瓦学术与创新研究院）；CSIR–National Institute of Science Communication and Policy Research, New Delhi, India（印度新德里国家科学传播与政策研究所）

Hanuman Verma, Kiho Im, Pranabesh Maji, Akshansh Gupta

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AI总结：

本文提出基于直觉模糊逻辑的UNet架构，用于提升脑图像分割中对不确定性的处理能力。

摘要：

准确分割MRI脑图像对于图像分析、神经系统疾病诊断和医学图像计算至关重要。在深度学习方法中，卷积神经网络（CNNs），尤其是UNet，被广泛应用于医学图像分割。然而，由于脑图像中的部分体积效应，难以处理不确定性。为克服这一限制，我们提出了一种增强框架，称为带有直觉模糊逻辑的UNet（IF-UNet），该框架将直觉模糊逻辑纳入UNet。该模型以隶属度、非隶属度和犹豫度处理输入数据，使其能够更好地处理由于部分体积效应和边界不确定性导致的组织模糊性。所提出的架构在互联网脑分割存储库（IBSR）数据集上进行评估，其性能通过准确率、Dice系数和交并比（IoU）计算。实验结果证实，IF-UNet在处理脑图像不确定性时提高了分割质量。

英文摘要：

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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