用模糊逻辑设计新损失函数，提升脑部MRI图像分割的准确性与可靠性。
An Uncertainty-Aware Loss Function Incorporating Fuzzy Logic: Application to MRI Brain Image Segmentation
- 融合模糊逻辑与交叉熵，动态处理像素分类不确定性。
- 在IBSR和OASIS数据集上，性能优于传统交叉熵损失。
- 适合对医学图像分割可靠性要求高的研究与临床应用。
准确的脑部图像分割在神经疾病检测与医学图像计算中至关重要。深度学习中，损失函数对模型优化极为关键。本文提出一种结合模糊逻辑的新损失函数，用于处理脑部MRI图像分割中的不确定性问题。该函数融合了经典的类别交叉熵（CCE）与基于模糊逻辑的模糊熵。通过引入模糊逻辑，损失函数能够有效建模像素分类的内在不确定性。在公开基准数据集IBSR和OASIS上，采用U-Net与U-Net++架构进行评估。实验结果表明，使用该损失函数训练的模型在多种性能指标上均优于仅使用CCE的情况。此外，该方法在训练过程中有效提升了分割精度并合理处理了有意义的不确定性。结果表明，该方法不仅改善了分割效果，还增强了模型预测的可靠性。
原文摘要 · Abstract (English)
Accurate brain image segmentation, particularly for distinguishing various tissues from magnetic resonance imaging (MRI) images, plays a pivotal role in finding the neurological dis ease and medical image computing. In deep learning approaches, loss functions are very crucial for optimizing the model. In this study, we introduce a novel loss function integrating fuzzy logic to deals uncertainty issues in brain image segmentation into various tissues. It integrates the well-known categorical cross-entropy (CCE) loss function and fuzzy entropy based on fuzzy logic. By employing fuzzy logic, this loss function accounts for the inherent uncertainties in pixel classifications. The proposed loss function has been evaluated on two publicly available benchmark datasets, IBSR and OASIS, using two widely recognised architectures, U-Net and U-Net++. Experimental results demonstrate that the trained model with proposed loss function provided better results in comparison to the CCE optimisation function in terms of various performance metrics. Additionally, it effectively enhances segmentation performance while handling meaningful uncer tainty during training. The findings suggest that this approach not only improves segmentation outcomes but also contributes to the reliability of model predictions.
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