AI 中文总结
提出FSS-UBrain区域级单样本分割框架，利用支持切片分别条件化分割WT、TC和ET，在BraTS数据集上取得领先性能，验证目标条件化少样本分割的有效性。
AI 中文摘要
在有限标注、跨队列变异和严重目标稀疏性条件下，从多模态磁共振成像中准确勾画整个肿瘤（WT）、肿瘤核心（TC）和增强肿瘤（ET）仍然具有挑战性。我们提出FSS-UBrain，一种区域级单样本分割框架，利用标记的阳性支持切片分别对WT、TC和ET进行二元查询分割的条件化。支持派生的前景和背景描述符引导查询特征适应、瓶颈交互、解码器侧重建和边界细化。情景训练额外纳入硬负样本和全负查询，并采用空查询正则化，以在所选区域缺失时抑制虚假前景激活。尽管推理在二维支持-查询切片对上进行，但检查点选择、阈值校准和最终评估均在体积重建后执行。FSS-UBrain在保留的BraTS 2020分割集以及BraTS 2023和BraTS-Africa的目标支持跨队列协议下进行评估。用作目标支持的病例被排除在查询队列之外，且不进行目标域微调或测试时参数更新。在BraTS 2020上，FSS-UBrain对WT、TC和ET分别实现了89.82%、82.14%和77.42%的体积Dice分数，相应的95百分位豪斯多夫距离（HD95）值为11.12、9.01和4.46毫米。在BraTS 2023和BraTS-Africa上，与所比较的少样本方法相比，它还实现了最高的平均Dice和最低的有限配对平均HD95点估计。这些发现支持目标条件化的少样本分割，同时强调了对支持选择和队列特定变异的敏感性。
英文摘要
Accurate delineation of whole tumor (WT), tumor core (TC), and enhancing tumor (ET) from multimodal magnetic resonance imaging remains challenging under limited annotation, cross-cohort variation, and severe target sparsity. We propose FSS-UBrain, a region-wise one-shot segmentation framework that uses a labeled positive support slice to condition binary query segmentation separately for WT, TC, and ET. Support-derived foreground and background descriptors guide query-feature adaptation, bottleneck interaction, decoder-side reconstruction, and boundary refinement. Episodic training additionally incorporates hard-negative and fully negative queries with empty-query regularization to suppress spurious foreground activation when the selected region is absent. Although inference operates on two-dimensional support--query slice pairs, checkpoint selection, threshold calibration, and final evaluation are performed after volumetric reconstruction. FSS-UBrain is evaluated on a held-out BraTS 2020 split and under target-supported cross-cohort protocols on BraTS 2023 and BraTS-Africa. Cases used as target support are excluded from the query cohorts, and no target-domain fine-tuning or test-time parameter updates are performed. On BraTS 2020, FSS-UBrain achieves volumetric Dice scores of 89.82%, 82.14%, and 77.42% for WT, TC, and ET, respectively, with corresponding 95th-percentile Hausdorff distance (HD95) values of 11.12, 9.01, and 4.46 mm. It also achieves the highest mean Dice and lowest finite-pair mean HD95 point estimates on BraTS 2023 and BraTS-Africa among the compared few-shot methods. These findings support target-conditioned few-shot segmentation while highlighting sensitivity to support selection and cohort-specific variation.
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