# BAP-MOS: Bandit-Based Adaptive Prompting for Boundary-Sensitive Multi-Organ Segmentation

arXiv: https://arxiv.org/abs/2608.08191 · HTML: https://mlsanity.com/paper/2608.08191/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 36 references that are in the map.
- First posted on arXiv 8 Aug 2026.

## Builds on (most cited first, 8 of 8)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Segment Anything Model for Medical Image Analysis: an Experimental Study](https://mlsanity.com/paper/2304.10517/) (2023)
- [Coordinate Descent Algorithms](https://mlsanity.com/paper/1502.04759/) (2015)
- [Boundary loss for highly unbalanced segmentation](https://mlsanity.com/paper/1812.07032/) (2018)
- [Reducing the Hausdorff Distance in Medical Image Segmentation with Convolutional Neural Networks](https://mlsanity.com/paper/1904.10030/) (2019)
- [Computer-Vision Benchmark Segment-Anything Model (SAM) in Medical Images: Accuracy in 12 Datasets](https://mlsanity.com/paper/2304.09324/) (2023)
- [AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation](https://mlsanity.com/paper/2308.14936/) (2023)
- [Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation](https://mlsanity.com/paper/2306.08958/) (2023)

## Abstract (arXiv)

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