ASTRA-Net：用于药物诱导睡眠内镜分割的特定解剖结构转移与表示对齐
ASTRA-Net: Anatomy-Specific Transfer and Representation Alignment for Drug-Induced Sleep Endoscopy Segmentation
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中文总结 AI 辅助
针对像素级DISE注释稀缺、手动勾勒轮廓限制定量评估可扩展性的问题，开发ASTRA-Net，通过虚拟与真实帧特征对齐及多目标监督微调解码器，在真实注释有限时支持DISE边界描绘，取得良好分割和分类准确率。
中文摘要 AI 辅助
定量药物诱导睡眠内镜检查（DISE）需要特定解剖层面的可靠气道边界。像素级DISE注释稀缺，手动勾勒轮廓限制了定量评估的可扩展性。为解决此限制，我们开发了ASTRA-Net用于在真实注释有限的情况下进行已知平面DISE分割。第一阶段对齐来自计算机断层扫描衍生的14250个未标记虚拟内镜帧和真实DISE帧的中间ConvNeXt-Base表示，虚拟图像仅用于特征对齐。第二阶段在401个真实注释帧上微调四个独立的UNet++解码器，结构化零掩码监督约束不兼容平面输出和无效帧。六种对齐配置使用最大均值差异、域对抗学习或两者目标。在100帧的保留评估集上，五模型仅MMD分割集成的平均Dice为0.8927，95%图像级自举区间为0.8631至0.9160，平均交并比为0.8239。相同对齐配置的分类启用变体在相同保留帧上达到受限四平面top-1准确率0.92。这些结果表明，当真实注释有限时，ASTRA-Net可以支持帧级、特定平面的DISE边界描绘。
英文摘要
Quantitative drug-induced sleep endoscopy (DISE) requires reliable airway boundaries at specific anatomical levels. Pixel-level DISE annotations are scarce, and manual contouring limits the scalability of quantitative assessment. To address this limitation, we developed ASTRA-Net for known-plane DISE segmentation with limited real annotations. Stage 1 aligned intermediate ConvNeXt-Base representations from 14,250 unlabeled virtual endoscopy frames derived from computed tomography and real DISE frames. Virtual images were used only for feature alignment. Stage 2 fine-tuned four independent UNet++ decoders on 401 real annotated frames. Structured zero-mask supervision constrained incompatible plane outputs and invalid frames. Six alignment configurations used maximum mean discrepancy, domain adversarial learning, or both objectives. On a hold-out evaluation set of 100 frames, the five-model MMD-only segmentation ensemble achieved a mean Dice of 0.8927, with a 95% image-level bootstrap interval of 0.8631 to 0.9160. The mean intersection over union was 0.8239. A classification- enabled variant of the same alignment configuration reached a restricted four-plane top-1 accuracy of 0.92 on the same hold-out frames. These results indicate that ASTRA-Net can support frame-level, plane-specific DISE boundary delineation when real annotations are limited.
发表机构
- Department of Otolaryngology, Peking University Third Hospital（北京大学第三医院耳鼻咽喉科）
- Institute of Medical Technology, Peking University Health Science Center（北京大学医学部医学技术研究所）
- National Biomedical Imaging Center, College of Future Technology, Peking University（北京大学未来技术学院国家生物医学影像中心）
机构由 AI 辅助整理，请以论文原文为准。
