AI 中文总结
该研究提出BAP-MOS自适应提示框架，将提示选择转化为多臂老虎机问题，在多器官超声分割基准上显著降低边界误差，泛化能力良好且无需修改模型主干。
AI 中文摘要
多器官超声分割仍面临挑战，尤其是需要联合描绘解剖学相邻结构时，即使Dice分数较高，局部边界误差也可能持续存在。为应对这些挑战，我们提出BAP-MOS（面向多器官分割的边界自适应提示），这是一种闭环自适应提示框架。BAP-MOS将提示选择表述为针对框、点及组合提示的器官特异性多臂老虎机问题。外层的树结构Parzen估计器（TPE）循环选择提示选择参数向量，内层的UCB-Tuned循环在微调过程中利用有界Dice-MSD-HD95验证探针奖励调整各器官的提示偏好。该框架还引入器官尺度的负提示环，以适配解剖学尺度下稀疏的提示几何结构，同时保持图像和提示编码器冻结，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，针对U-Net、nnU-Net、MedSAM、固定提示的SAM/MedSAM及自适应策略变体评估BAP-MOS。在该基准上，BAP-MOS取得Dice 0.982、HD95 0.482、MSD 0.204的结果，相较于最强的传统基线，HD95降低约48%，MSD降低45%。为验证框架的泛化能力，我们使用MedSAM及其自适应策略变体在外部PFUS1盆底超声语料库上进行测试，结果良好。这些结果表明，自适应提示分配是提升边界敏感型多器官超声分割的有效机制，且无需修改基础模型主干。源代码可获取于：this https URL
英文摘要
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
Comments9 pages, 5 figures. Source code available at https://github.com/SatvikPraveen/BAP-MOS
