用AI自动识别宫颈口，让无窥器宫颈筛查更简单可靠
Automated Cervical Os Segmentation for Camera-Guided, Speculum-Free Screening
- 用视觉变换器模型分析阴道镜图像，实时分割宫颈口位置
- 在913帧数据上达到0.50的DICE分数和87%检测率
- 适合医疗资源匮乏地区，支持非专业人员操作
宫颈癌可预防，但筛查障碍仍阻碍消除目标。无需窥器的成像采样设备能提升可及性，尤其在资源有限地区，但需可靠的视觉引导。本研究评估深度学习方法在经阴道内镜图像中实时分割宫颈口的表现。使用国际癌症研究机构宫颈图像数据集中的913帧（200例）进行五种编码器-解码器架构比较，由妇科医生标注。通过十折交叉验证，采用交并比（IoU）、DICE、检测率和距离指标评估性能。基于外科视频预训练的视觉变换器模型EndoViT/DPT取得最高DICE值（0.50 ± 0.31）与检测率（0.87 ± 0.33），优于基于CNN的方法。外部模拟数据验证显示，在21.5 FPS下具备鲁棒性，支持实时应用。结果为将自动化宫颈口识别集成至无窥器宫颈筛查设备提供了基础，适用于高、低资源环境中的非专业使用者。
原文摘要 · Abstract (English)
Cervical cancer is highly preventable, yet persistent barriers to screening limit progress toward elimination goals. Speculum-free devices that integrate imaging and sampling could improve access, particularly in low-resource settings, but require reliable visual guidance. This study evaluates deep learning methods for real-time segmentation of the cervical os in transvaginal endoscopic images. Five encoder-decoder architectures were compared using 913 frames from 200 cases in the IARC Cervical Image Dataset, annotated by gynaecologists. Performance was assessed using IoU, DICE, detection rate, and distance metrics with ten-fold cross-validation. EndoViT/DPT, a vision transformer pre-trained on surgical video, achieved the highest DICE (0.50 \pm 0.31) and detection rate (0.87 \pm 0.33), outperforming CNN-based approaches. External validation with phantom data demonstrated robust segmentation under variable conditions at 21.5 FPS, supporting real-time feasibility. These results establish a foundation for integrating automated os recognition into speculum-free cervical screening devices to support non-expert use in both high- and low-resource contexts.
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