# False Negative/Positive Control for SAM on Noisy Medical Images

arXiv: https://arxiv.org/abs/2308.10382 · HTML: https://mlsanity.com/paper/2308.10382/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 18 references that are in the map.
- First posted on arXiv 20 Aug 2023.

## Builds on (most cited first, 15 of 15)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?](https://mlsanity.com/paper/1703.04977/) (2017)
- [Medical SAM Adapter: Adapting Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.12620/) (2023)
- [Customized Segment Anything Model for Medical Image Segmentation](https://mlsanity.com/paper/2304.13785/) (2023)
- [Track Anything: Segment Anything Meets Videos](https://mlsanity.com/paper/2304.11968/) (2023)
- [Inpaint Anything: Segment Anything Meets Image Inpainting](https://mlsanity.com/paper/2304.06790/) (2023)
- [Semantic-SAM: Segment and Recognize Anything at Any Granularity](https://mlsanity.com/paper/2307.04767/) (2023)
- [Segment Anything Model (SAM) for Digital Pathology: Assess Zero-shot Segmentation on Whole Slide Imaging](https://mlsanity.com/paper/2304.04155/) (2023)
- [SAM on Medical Images: A Comprehensive Study on Three Prompt Modes](https://mlsanity.com/paper/2305.00035/) (2023)
- [SAM.MD: Zero-shot medical image segmentation capabilities of the Segment Anything Model](https://mlsanity.com/paper/2304.05396/) (2023)
- [Input Augmentation with SAM: Boosting Medical Image Segmentation with Segmentation Foundation Model](https://mlsanity.com/paper/2304.11332/) (2023)
- [All-in-SAM: from Weak Annotation to Pixel-wise Nuclei Segmentation with Prompt-based Finetuning](https://mlsanity.com/paper/2307.00290/) (2023)
- [SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2305.11012/) (2023)
- [Temporally-Extended Prompts Optimization for SAM in Interactive Medical Image Segmentation](https://mlsanity.com/paper/2306.08958/) (2023)
- [Box-Adapt: Domain-Adaptive Medical Image Segmentation using Bounding BoxSupervision](https://mlsanity.com/paper/2108.08432/) (2021)

## Built on by (most cited first, 12 of 12)

- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM](https://mlsanity.com/paper/2409.00924/) (2024)
- [PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts](https://mlsanity.com/paper/2404.15028/) (2024)
- [Zero-Shot Surgical Tool Segmentation in Monocular Video Using Segment Anything Model 2](https://mlsanity.com/paper/2408.01648/) (2024)
- [WS-SfMLearner: Self-supervised Monocular Depth and Ego-motion Estimation on Surgical Videos with Unknown Camera Parameters](https://mlsanity.com/paper/2308.11776/) (2023)
- [SAMSNeRF: Segment Anything Model (SAM) Guides Dynamic Surgical Scene Reconstruction by Neural Radiance Field (NeRF)](https://mlsanity.com/paper/2308.11774/) (2023)
- [Enhancing the Reliability of Segment Anything Model for Auto-Prompting Medical Image Segmentation with Uncertainty Rectification](https://mlsanity.com/paper/2311.10529/) (2023)
- [Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts](https://mlsanity.com/paper/2311.07806/) (2023)
- [Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution](https://mlsanity.com/paper/2505.07071/) (2025)
- [Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images](https://mlsanity.com/paper/2407.08020/) (2024)
- [Fully Automated SAM for Single-source Domain Generalization in Medical Image Segmentation](https://mlsanity.com/paper/2507.17281/) (2025)
- [PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound](https://mlsanity.com/paper/2408.05372/) (2024)

## Abstract (arXiv)

The Segment Anything Model (SAM) is a recently developed all-range foundation model for image segmentation. It can use sparse manual prompts such as bounding boxes to generate pixel-level segmentation in natural images but struggles in medical images such as low-contrast, noisy ultrasound images. We propose a refined test-phase prompt augmentation technique designed to improve SAM's performance in medical image segmentation. The method couples multi-box prompt augmentation and an aleatoric uncertainty-based false-negative (FN) and false-positive (FP) correction (FNPC) strategy. We evaluate the method on two ultrasound datasets and show improvement in SAM's performance and robustness to inaccurate prompts, without the necessity for further training or tuning. Moreover, we present the Single-Slice-to-Volume (SS2V) method, enabling 3D pixel-level segmentation using only the bounding box annotation from a single 2D slice. Our results allow efficient use of SAM in even noisy, low-contrast medical images. The source code will be released soon.