发表机构
Heidelberg University（海德堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对生物医学图像分割边界不可靠问题，提出不确定性引导的RABR-Net两阶段框架，通过门控残差细化器修正不确定边界像素，在血涂片数据集上显著提升边界Dice和HD95。
AI 中文摘要
准确的生物医学图像分割不仅需要较高的全局重叠度，还需要对具有临床意义的边界进行可靠描绘。在血涂片显微镜检查中，细胞质和细胞核轮廓为下游形态学分析提供了结构基础；然而，深度分割模型即使在取得较高Dice分数的情况下，在模糊边界区域附近仍可能保持不确定或过度自信。本工作提出了一种可靠性感知边界细化网络（RABR-Net），这是一个用于可信图像分割的两阶段框架。首先，一个强大的UNet++ EfficientNet-B4基础分割器生成初始类别概率和logits。随后，将预测熵、测试时增强方差、边际不确定性、概率梯度以及软边界线索组合成一种边界感知的可靠性表示。该表示引导一个门控残差细化器，选择性地修正不确定的边界像素，同时保留基础预测中的置信区域。该框架通过重叠准确率、类别Dice、边界Dice、HD95/ASSD、校准、风险-覆盖分析、图像扰动下的鲁棒性、定性校正图以及配对统计检验进行评估。在保留测试集上，与缓存的基础预测相比，所提方法将Dice从0.9602提升至0.9614，边界Dice从0.3448提升至0.3611，HD95从3.0354降至2.8274。统计分析确认了Dice、边界Dice和HD95的显著改进。定性结果表明，学习到的门控集中在不确定的细胞质和细胞核边界周围，校正图确认了局部边界细化。尽管细化后校准并未自动改善，但所提框架为边界敏感的医学图像分割提供了一种可解释且以可靠性为中心的策略。
英文摘要
Accurate biomedical image segmentation requires not only high global overlap but also reliable delineation of clinically meaningful boundaries. In blood-smear microscopy, cytoplasm and nucleus contours provide the structural basis for downstream morphology analysis; however, deep segmentation models may remain uncertain or overconfident near ambiguous boundary regions even when achieving strong Dice scores. This work proposes a Reliability-Aware Boundary Refinement Network (RABR-Net), a two-stage framework for trustworthy image segmentation. A strong UNet++ EfficientNet-B4 base segmenter first produces initial class probabilities and logits. Predictive entropy, test-time augmentation variance, margin uncertainty, probability gradients, and soft boundary cues are then combined into a boundary-aware reliability representation. This representation guides a gated residual refiner that selectively corrects uncertain boundary pixels while preserving confident regions of the base prediction. The framework is evaluated using overlap accuracy, class-wise Dice, Boundary Dice, HD95/ASSD, calibration, risk--coverage analysis, robustness under image perturbations, qualitative correction maps, and paired statistical testing. On the held-out test set, the proposed method improves Dice from 0.9602 to 0.9614, Boundary Dice from 0.3448 to 0.3611, and HD95 from 3.0354 to 2.8274 compared with the cached base prediction. Statistical analysis confirms significant improvements in Dice, Boundary Dice, and HD95. Qualitative results show that the learned gate concentrates around uncertain cytoplasm and nucleus boundaries, and correction maps confirm localized boundary refinement. Although calibration does not automatically improve after refinement, the proposed framework provides an interpretable and reliability-focused strategy for boundary-sensitive biomedical image segmentation.
