首个多任务干眼症泪膜破裂数据集，支持自动分割与临床分析
TFM Dataset: A Novel Multi-task Dataset and Integrated Pipeline for Automated Tear Film Break-Up Segmentation
- 构建含15段视频的多任务数据集，标注分类、角膜环定位与像素级破裂分割
- 提出TF-Net模型，在6247帧上实现高精度实时分割，兼顾效率与准确率
- 设计端到端自动化流程，适合眼科诊断研究与医疗设备开发人员
泪膜破裂（TFBU）分析对干眼症诊断至关重要，但自动化分割因缺乏标注数据集和集成方案而困难。本文提出首个综合性多任务泪膜分析数据集——TFM数据集，包含15个高分辨率视频（共6247帧），标注了三项视觉任务：帧级分类（'清晰'、'闭合'、'破裂'、'模糊'）、Placido Ring检测和像素级TFBU区域分割。基于此数据集，我们首次提出TF-Net，一种结合MobileOne-mini主干与重参数化技术的高效分割模型，采用增强特征金字塔网络，在准确率与计算效率间取得良好平衡，适用于实时临床应用。我们在TFM分割子集上对比多种先进医学图像分割模型，建立基准性能。进一步设计了TF-Collab，一种新型集成实时分析流程，协同利用三个任务训练的模型：依次完成帧分类以确定破裂时间、瞳孔区域定位以标准化输入、最终执行TFBU分割，实现全流程自动化。实验表明，所提方法有效，为眼表诊断研究奠定基础。代码与数据集已开源。
原文摘要 · Abstract (English)
Tear film break-up (TFBU) analysis is critical for diagnosing dry eye syndrome, but automated TFBU segmentation remains challenging due to the lack of annotated datasets and integrated solutions. This paper introduces the Tear Film Multi-task (TFM) Dataset, the first comprehensive dataset for multi-task tear film analysis, comprising 15 high-resolution videos (totaling 6,247 frames) annotated with three vision tasks: frame-level classification ('clear', 'closed', 'broken', 'blur'), Placido Ring detection, and pixel-wise TFBU area segmentation. Leveraging this dataset, we first propose TF-Net, a novel and efficient baseline segmentation model. TF-Net incorporates a MobileOne-mini backbone with re-parameterization techniques and an enhanced feature pyramid network to achieve a favorable balance between accuracy and computational efficiency for real-time clinical applications. We further establish benchmark performance on the TFM segmentation subset by comparing TF-Net against several state-of-the-art medical image segmentation models. Furthermore, we design TF-Collab, a novel integrated real-time pipeline that synergistically leverages models trained on all three tasks of the TFM dataset. By sequentially orchestrating frame classification for BUT determination, pupil region localization for input standardization, and TFBU segmentation, TF-Collab fully automates the analysis. Experimental results demonstrate the effectiveness of the proposed TF-Net and TF-Collab, providing a foundation for future research in ocular surface diagnostics. Our code and the TFM datasets are available at https://github.com/glory-wan/TF-Net
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