基于DeepLab和可变形卷积的肝脏病理环境分割方法
A DeepLab and Deformable Convolution-Based Segmentation Method for Liver Pathological Microenvironment

深度学习驱动的图像分割在病理图像分析领域成效显著，但现有研究多集中于单一病灶的识别分析，缺乏对肝脏整体病理微环境的系统性定量评估。针对此问题，本文提出一种融合自监督学习与可变形卷积的肝脏病理图像分割框架。首先基于苏木精–伊红(H&E)染色特点构造了一个包含7类关键病理结构的肝脏病理分割数据集。其次，在方法层面创新性地采用DINO自监督学习框架对ResNet-50进行病理图像预训练，通过教师–学生动态特征对齐机制缓解模型坍塌问题，并引入多尺度增强策略提升特征表征能力。进一步，在DeepLab v3+模型的空洞空间金字塔池化(ASPP)模块中集成可变形卷积，通过动态调整感受野实现对不规则病灶的局部细节捕捉与全局语义信息融合。实验表明，改进模型在仅需少量标注数据的条件下，IoU与Dice指标显著优于U-Net与原始DeepLab v3+，其中肝实质区域分割精度达0.84 (IoU)与0.90 (Dice)，有效解决了传统方法对复杂边界及非规则病灶的分割鲁棒性不足难题。本研究为肝脏病理微环境的系统性定量评估提供了兼具数学可解释性与临床实用性的计算工具，推动人工智能病理诊断向小样本学习与多维度特征协同建模方向发展。
Deep learning-driven image segmentation has achieved remarkable success in pathological image analysis. However, existing studies predominantly focus on single-lesion identification while lacking systematic quantitative evaluation of the holistic liver pathological microenvironment. To address this gap, we propose a liver pathology image segmentation framework integrating self-supervised learning with deformable convolutions. First, we construct a liver pathology segmentation dataset comprising seven critical pathological structures based on hematoxylin-eosin (H&E) staining characteristics. Methodologically, we innovatively employ the DINO self-supervised framework for pathological image pre-training of ResNet-50, which alleviates model collapse through a teacher-student dynamic feature alignment mechanism and enhances feature representation via multi-scale augmentation strategies. Furthermore, we integrate deformable convolutions into the Atrous Spatial Pyramid Pooling (ASPP) module of DeepLab v3+, enabling dynamic receptive field adjustment to capture irregular lesion details while preserving global semantic coherence. Experimental results demonstrate that our improved model significantly outperforms U-Net and vanilla DeepLab v3+ in IoU and Dice metrics with minimal annotation requirements, achieving segmentation accuracy of 0.84 (IoU) and 0.90 (Dice) for hepatic parenchyma regions. This effectively resolves the robustness challenges in traditional methods for complex boundaries and irregular lesions. Our study provides a mathematically interpretable and clinically practical computational tool for systematic quantification of liver pathological microenvironments, advancing AI-powered pathological diagnosis towards few-shot learning and multi-dimensional feature co-modeling paradigms.