Abstract: Weak boundaries, low-contrast lesions, and imaging artifacts often cause localization shifts, missed regions, and over-segmentation in two-dimensional medical image segmentation. Meanwhile, patch-level semantics from a frozen vision foundation model do not fully correspond to pixel-level medical structures. To address these problems, a DINOv3 prior-guided parameter-efficient network, termed HiFi-DinoMECNet, is proposed. The network employs frozen DINOv3-S/16 as the semantic encoder and trains only the spatial adaptation components and decoder, retaining self-supervised semantic priors while reducing the number of parameters updated for the target task. First, patch tokens are extracted from the 3rd, 6th, 9th, and 12th Transformer blocks. Class and register tokens are removed, and four pseudo-multiscale semantic interfaces are constructed through token-to-map transformation and scale alignment, allowing features at different semantic depths to interact progressively with the spatial branch. Second, an encoder-side semantic-spatial fidelity injection module (SSFI) uses shallow convolutional features to generate spatial queries, predict local sampling offsets and aggregation weights, and perform spatially guided local sampling on frozen semantic feature maps. This process strengthens semantic responses around weak boundaries, textures, and local structures. Third, a multi-scale efficient collaborative attention decoder (MECAD) successively performs upsampling calibration, semantic-guided gating, and multi-scale contextual aggregation to suppress shallow noise and spurious boundaries and progressively recover high-resolution segmentation results. Experiments are conducted on four public two-dimensional medical image datasets: ISIC 2017, ISIC 2018, BUSI, and DSB18. All methods are independently trained under fixed data splits using the same five random seeds, and results are reported as mean ± standard deviation. Key hyperparameters are selected on an independent validation set, while the test set is used only after the final model configuration has been fixed. HiFi-DinoMECNet achieves Dice similarity coefficients of 91.04%, 91.08%, 83.18%, and 93.12% on the four datasets, respectively. On the ISIC 2018 test set, it obtains a mean intersection over union of 83.18%±0.12%, a Dice similarity coefficient of 91.08%±0.08%, a BoundaryF1 score of 85.42%±0.11%, and an HD95 of 8.34±0.15 pixels. Ablation experiments show that SSFI and MECAD contribute to encoder-side semantic-spatial adaptation and decoder-side feature reconstruction, respectively, and jointly improve region overlap and boundary quality. At an input resolution of 224×224, the model contains 27.64 M total parameters and 6.28 M trainable parameters, requires 5.12 G FLOPs, and achieves 57.20±1.25 FPS. These results indicate that HiFi-DinoMECNet provides a practical balance between segmentation accuracy and adaptation cost and offers a feasible parameter-efficient transfer strategy for applying frozen vision foundation models to two-dimensional medical image segmentation.

摘要： 针对二维医学图像分割中弱边界、低对比病灶及成像伪影易造成定位偏移、漏分和过分割，以及冻结视觉基础模型的图像块级语义与医学像素级空间结构对应不足的问题，提出DINOv3先验引导的参数高效医学图像分割网络HiFi-DinoMECNet。网络以冻结的DINOv3-S/16为语义编码主干，仅训练空间适配结构和解码器，在保留自监督语义先验的同时降低任务训练阶段的参数更新规模。首先，从DINOv3第3、6、9和12个Transformer块提取图像块标记，去除类别标记和寄存标记，通过标记-特征图转换及尺度对齐构建四级伪多尺度语义接口，使不同语义深度的特征能够与后续空间分支逐级对应。其次，在编码端设计语义-空间保真注入模块SSFI，利用浅层卷积特征生成空间查询，预测局部采样偏移和聚合权重，并在冻结语义特征图上执行空间引导的局部采样，从而增强弱边界、纹理及局部结构区域的语义响应。再次，在解码端构建多尺度高效协同注意解码器MECAD，依次完成上采样校准、语义门控筛选和多尺度上下文聚合，以抑制浅层噪声与伪边界并逐级恢复高分辨率分割结果。实验在ISIC 2017、ISIC 2018、BUSI和DSB18四个公开二维医学图像数据集上开展，所有方法在固定数据划分下采用相同的5个随机种子独立训练，并报告均值±标准差。关键超参数依据独立验证集选择，测试集仅用于最终模型配置确定后的性能评价。HiFi-DinoMECNet在四个数据集上的Dice相似系数分别为91.04%、91.08%、83.18%和93.12%。在ISIC 2018测试集上，模型的平均交并比、Dice相似系数、BoundaryF1和HD95分别为83.18%±0.12%、91.08%±0.08%、85.42%±0.11%和8.34±0.15像素。消融实验表明，SSFI与MECAD分别作用于编码端语义-空间适配和解码端特征重构，二者共同改善区域重叠和边界质量。在224×224输入分辨率下，模型总参数量为27.64M，可训练参数量为6.28M，浮点运算次数为5.12G，推理速度为57.20±1.25FPS。结果表明，该方法能够在分割精度与适配成本之间取得合理平衡，为冻结视觉基础模型在二维医学图像分割中的参数高效迁移提供可行方案。