用模糊逻辑提升医学图像分割边界清晰度与不确定性控制
CLFSeg: A Fuzzy-Logic based Solution for Boundary Clarity and Uncertainty Reduction in Medical Image Segmentation
- 融合卷积层与模糊逻辑的FC模块，增强特征提取与边界处理能力
- 在4个公开数据集上超越现有最先进模型，尤其在小目标和边界区域表现优异
- 兼顾计算效率，适合临床实际部署，适用于肠息肉与心脏等分割任务
准确的息肉和心脏分割对癌症类疾病的早期诊断与治疗规划至关重要。传统基于卷积神经网络（CNN）的模型普遍存在泛化性差、鲁棒性不足及难以处理不确定性的缺陷，影响分割效果。为此，本文提出基于编码器-解码器结构的CLFSeg框架，其核心为融合卷积层与模糊逻辑的模糊卷积（Fuzzy-Convolutional, FC）模块，可同时捕捉局部与全局特征，有效降低边界区域的不确定性、噪声与模糊性，且保持高效计算。针对类别不平衡问题，结合二值交叉熵（BCE）与Dice损失，强化对微小区域及边界区域的关注。在CVC-ColonDB、CVC-ClinicDB、EtisLaribPolypDB和ACDC四个公开数据集上的大量实验与可视化分析表明，CLFSeg显著优于现有最先进方法，精准聚焦于解剖结构中的关键区域。该模型在提升性能的同时保障计算效率，具备实际临床诊断应用潜力。
原文摘要 · Abstract (English)
Accurate polyp and cardiac segmentation for early detection and treatment is essential for the diagnosis and treatment planning of cancer-like diseases. Traditional convolutional neural network (CNN) based models have represented limited generalizability, robustness, and inability to handle uncertainty, which affects the segmentation performance. To solve these problems, this paper introduces CLFSeg, an encoder-decoder based framework that aggregates the Fuzzy-Convolutional (FC) module leveraging convolutional layers and fuzzy logic. This module enhances the segmentation performance by identifying local and global features while minimizing the uncertainty, noise, and ambiguity in boundary regions, ensuring computing efficiency. In order to handle class imbalance problem while focusing on the areas of interest with tiny and boundary regions, binary cross-entropy (BCE) with dice loss is incorporated. Our proposed model exhibits exceptional performance on four publicly available datasets, including CVC-ColonDB, CVC-ClinicDB, EtisLaribPolypDB, and ACDC. Extensive experiments and visual studies show CLFSeg surpasses the existing SOTA performance and focuses on relevant regions of interest in anatomical structures. The proposed CLFSeg improves performance while ensuring computing efficiency, which makes it a potential solution for real-world medical diagnostic scenarios. Project page is available at https://visdomlab.github.io/CLFSeg/
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