用分形原理增强边界感知，提升医学图像分割鲁棒性
ConvNeXt-FD: A Fractal-Based Deep Model for Robust Biomedical Image Segmentation
- 基于ConvNeXt的编码器-解码器架构，融合分形维数引导的边界正则化
- 在6个数据集上均优于现有方法，尤其在边界精度和形状保真度上显著提升
- 适合处理噪声大、形态复杂的医学影像，对临床诊断有实用价值
医学图像分割在疾病诊断与治疗规划中至关重要，可精确勾画解剖结构与病灶区域。尽管取得进展，不同成像模态下的固有变异、噪声及复杂形态仍带来挑战。本文提出ConvNeXt-FD，一种基于U-Net-like框架的新型深度学习模型，采用强大的ConvNeXt作为主干网络，并引入混合损失函数，结合Dice系数与受可微分分形维数启发的边界感知正则项，以增强对物体边界的敏感性和形状保真度。我们在六个生物医学数据集上进行严格评估：BUSI（乳腺超声）、DDTI（甲状腺超声）、FluoCells（荧光细胞）、IDRiD（糖尿病视网膜病变视盘分割）、ISIC2018（皮肤病变）和MoNuSeg（细胞核分割）。实验结果表明，当使用ImageNet预训练权重初始化时，ConvNeXt-FD在多种指标（包括Dice、Jaccard、准确率、灵敏度、特异性、假阳性率）上表现优异，甚至超越现有先进方法。将ConvNeXt作为强编码器并结合边界感知正则化，有效捕捉高层语义特征与细粒度边界细节，显著提升复杂医学场景下的分割精度与可靠性。
原文摘要 · Abstract (English)
Biomedical image segmentation is a critical task in medical diagnosis and treatment planning, enabling precise delineation of anatomical structures and pathological regions. Despite significant advancements, challenges persist due to the inherent variability, noise, and complex morphology present in diverse medical imaging modalities. This paper introduces ConvNeXt-FD, a novel deep learning architecture for robust biomedical image segmentation, built upon a U-Net-like encoder-decoder framework leveraging the powerful ConvNeXt backbone. Our approach integrates a hybrid loss function combining the Dice coefficient with a boundary-aware regularization term inspired by a differentiable formulation of Fractal Dimension, designed to enhance the model's sensitivity to object boundaries and shape fidelity. We rigorously evaluate ConvNeXt-FD across six distinct biomedical datasets: BUSI (Breast Ultrasound Images), DDTI (Thyroid Ultrasound Images), FluoCells (Fluorescent Cell Images), IDRiD (Diabetic Retinopathy Images for Optic Disc Segmentation), ISIC2018 (Skin Lesion Images), and MoNuSeg (Nuclei Segmentation). Experimental results demonstrate that ConvNeXt-FD, particularly when initialized with ImageNet pre-trained weights, achieves competitive and often superior performance compared to existing state-of-the-art methods across various metrics, including Dice, Jaccard, Accuracy, Sensitivity, Specificity, and False Positive Rate. The integration of ConvNeXt as a strong encoder, coupled with the boundary-aware regularization, proves effective in capturing both high-level semantic features and fine-grained boundary details, leading to more accurate and reliable segmentations in challenging biomedical contexts.
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