用强化学习动态优化提示，显著提升超声图像多器官边界分割精度。
BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation
- 将提示选择建模为多臂赌博机问题，自适应调整不同器官的提示类型与参数
- 在前列腺超声数据上实现Dice 0.982、HD95 0.482、MSD 0.204，较最强基线降低48%与45%
- 无需修改主干模型，可适配不同解剖尺度，适合医学影像边界敏感任务
多器官超声分割在需联合勾画解剖邻近结构时仍具挑战性，因局部边界误差可能在骰子分数较高时依然存在。为此，我们提出边界自适应提示框架BAP-MOS，采用闭环自适应提示机制。将提示选择建模为针对框、点及组合提示的器官特异性多臂赌博机问题，外层使用树状帕尔森估计器（TPE）选择提示参数向量，内层通过带调优的上置信界（UCB-Tuned）算法在微调过程中自适应调整各器官的提示偏好，基于受限的骰子-平均表面距离-95%豪斯多夫距离验证探针奖励。该框架引入器官缩放负提示环，以适应不同解剖尺度下的稀疏提示几何结构，同时保持图像与提示编码器冻结，仅更新掩码解码器。我们在合并的前列腺区域经直肠超声（TRUS）队列上评估BAP-MOS，对比U-Net、nnU-Net、MedSAM、固定提示的SAM/MedSAM以及自适应策略变体，在该基准上取得骰子分数0.982，HD95为0.482，MSD为0.204，相较最强传统基线分别降低48%和45%的HD95与MSD。为验证泛化能力，我们在外部盆底超声（PFUS1）数据集上测试了MedSAM及其自适应策略变体，结果表现良好。这些结果支持自适应提示分配是提升边界敏感多器官超声分割的有效机制，且无需修改基础模型主干。源代码见：https://github.com/SatvikPraveen/BAP-MOS
原文摘要 · Abstract (English)
Multi-organ ultrasound segmentation remains challenging when anatomically adjacent structures must be delineated jointly, as localized boundary errors can persist even when Dice scores are high. To address these challenges, we propose Boundary-Adaptive Prompting for Multi-Organ Segmentation (BAP-MOS), a closed-loop adaptive prompting framework. BAP-MOS formulates prompt selection as an organ-specific multi-armed bandit problem over box, point, and combined prompts. An outer Tree-structured Parzen Estimator (TPE) loop selects the prompt-selection parameter vector, while an inner UCB-Tuned loop adapts per-organ prompt preferences during fine-tuning using a bounded Dice--MSD--HD95 validation-probe reward. The framework further introduces an organ-scaled negative prompt ring to adapt sparse prompt geometry across anatomical scales, while keeping the image and prompt encoders frozen and updating only the mask decoder. We evaluate BAP-MOS on pooled prostate-region TRUS cohorts against U-Net, nnU-Net, MedSAM, fixed-prompt SAM/MedSAM, and adaptive policy variants. On this benchmark, BAP-MOS achieves Dice 0.982, HD95 0.482, and MSD 0.204, reducing HD95 by approximately 48% and MSD by 45% relative to the strongest conventional baseline. To verify the generalization ability of the framework, we tested it on the external PFUS1 pelvic-floor ultrasound corpus using MedSAM and its adaptive strategy variants, and the results were good. These results support adaptive prompt allocation as an effective mechanism for improving boundary-sensitive multi-organ ultrasound segmentation without modifying the foundation-model backbone. Source Code is available at: https://github.com/SatvikPraveen/BAP-MOS
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