为 cine 图像中的腺体病变分割适应基础模型以提高标注效率
Adapting Foundation Models for Annotation-Efficient Adnexal Mass Segmentation in Cine Images
AI总结：
本文提出一种标注效率高的分割框架，利用预训练的DINOv3基础视觉变压器主干，结合DPT解码器实现多尺度特征层次重组，取得优于U-Net等传统全监督基线的性能，且在数据稀缺情况下表现更优。
摘要：
通过超声波评估腺体病变是一项具有挑战性的临床任务，通常受到主观解释和显著的观察者间差异的阻碍。尽管自动分割是定量风险评估的基础步骤，但传统全监督卷积架构通常需要大量的像素级标注，并在医学影像中面临常见的域偏移问题。在本工作中，我们提出了一种标注效率高的分割框架，利用预训练的DINOv3基础视觉变压器主干的稳健语义先验。通过将此主干与Dense Prediction Transformer（DPT）风格的解码器相结合，我们的模型层次重组多尺度特征，以结合全局语义表示与细粒度空间细节。在包含7777个标注帧的临床数据集上评估，我们的方法在与U-Net、U-Net++、DeepLabV3和MAnet等传统全监督基线相比，取得了最先进的性能。具体而言，我们获得了0.945的Dice分数和改进的边界适应性，将95百分位Hausdorff距离减少了11.4%相对于最强的卷积基线。此外，我们进行了广泛的效率分析，证明我们的基于DINOv3的方法在数据稀缺情况下保持了显著更高的性能，即使在仅训练25%数据的情况下也能保持强劲的结果。这些结果表明，利用大规模自监督基础模型提供了一种有前景且数据高效的解决方案，用于数据受限的临床环境中的医学图像分割。项目仓库：https://github.com/FrancescaFati/MESA
英文摘要：
Adnexal mass evaluation via ultrasound is a challenging clinical task, often hindered by subjective interpretation and significant inter-observer variability. While automated segmentation is a foundational step for quantitative risk assessment, traditional fully supervised convolutional architectures frequently require large amounts of pixel-level annotations and struggle with domain shifts common in medical imaging. In this work, we propose a label-efficient segmentation framework that leverages the robust semantic priors of a pretrained DINOv3 foundational vision transformer backbone. By integrating this backbone with a Dense Prediction Transformer (DPT)-style decoder, our model hierarchically reassembles multi-scale features to combine global semantic representations with fine-grained spatial details. Evaluated on a clinical dataset of 7,777 annotated frames from 112 patients, our method achieves state-of-the-art performance compared to established fully supervised baselines, including U-Net, U-Net++, DeepLabV3, and MAnet. Specifically, we obtain a Dice score of 0.945 and improved boundary adherence, reducing the 95th-percentile Hausdorff Distance by 11.4% relative to the strongest convolutional baseline. Furthermore, we conduct an extensive efficiency analysis demonstrating that our DINOv3-based approach retains significantly higher performance under data starvation regimes, maintaining strong results even when trained on only 25% of the data. These results suggest that leveraging large-scale self-supervised foundations provides a promising and data-efficient solution for medical image segmentation in data-constrained clinical environments. Project Repository: https://github.com/FrancescaFati/MESA
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
