# An Edge-aware Prompt-enhanced SAM for Ultrasound Image Segmentation

arXiv: https://arxiv.org/abs/2607.07240 · HTML: https://mlsanity.com/paper/2607.07240/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 35 references that are in the map.
- First posted on arXiv 8 Jul 2026.

## Builds on (most cited first, 22 of 22)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [UNet++: A Nested U-Net Architecture for Medical Image Segmentation](https://mlsanity.com/paper/1807.10165/) (2018)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers](https://mlsanity.com/paper/2012.15840/) (2020)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [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)
- [TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation](https://mlsanity.com/paper/2102.08005/) (2021)
- [Deep Learning for Segmentation using an Open Large-Scale Dataset in 2D Echocardiography](https://mlsanity.com/paper/1908.06948/) (2019)
- [CE-Net: Context Encoder Network for 2D Medical Image Segmentation](https://mlsanity.com/paper/1903.02740/) (2019)
- [Segment Anything Model for Medical Image Segmentation: Current Applications and Future Directions](https://mlsanity.com/paper/2401.03495/) (2024)
- [CA-Net: Comprehensive Attention Convolutional Neural Networks for Explainable Medical Image Segmentation](https://mlsanity.com/paper/2009.10549/) (2020)
- [AAU-net: An Adaptive Attention U-net for Breast Lesions Segmentation in Ultrasound Images](https://mlsanity.com/paper/2204.12077/) (2022)
- [Beyond Adapting SAM: Towards End-to-End Ultrasound Image Segmentation via Auto Prompting](https://mlsanity.com/paper/2309.06824/) (2023)
- [5%>100%: Breaking Performance Shackles of Full Fine-Tuning on Visual Recognition Tasks](https://mlsanity.com/paper/2408.08345/) (2024)
- [CC-SAM: SAM with Cross-feature Attention and Context for Ultrasound Image Segmentation](https://mlsanity.com/paper/2408.00181/) (2024)
- [Early Myocardial Infarction Detection over Multi-view Echocardiography](https://mlsanity.com/paper/2111.05790/) (2021)
- [Adapting Vision Foundation Models for Real-time Ultrasound Image Segmentation](https://mlsanity.com/paper/2503.24368/) (2025)
- [UniUltra: Interactive Parameter-Efficient SAM2 for Universal Ultrasound Segmentation](https://mlsanity.com/paper/2511.15771/) (2025)
- [PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical Image Segmentation](https://mlsanity.com/paper/2501.06692/) (2025)
- [SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation](https://mlsanity.com/paper/2406.14819/) (2024)

## Abstract (arXiv)

Ultrasound image segmentation is essential for delineating anatomical structures and lesions, providing the foundation for accurate diagnosis. While the Segment Anything Model (SAM) has demonstrated remarkable success on natural images, its performance on ultrasound data is often hindered by poor boundary delineation. To address this limitation, we propose EP-SAM, an edge-aware and prompt-enhanced adaptation of SAM. Specifically, we leverage multi-block feature extraction from the image encoder to enrich coarse-to-fine semantic representations, while edge-aware supervision of the image encoder improves robustness to contour ambiguity and speckle noise. By integrating these complementary cues, EP-SAM generates high-quality prompts that effectively guide the model toward target regions of interest. Experimental results on multiple benchmarks demonstrate that EP-SAM consistently outperforms existing SAM-based methods.