# BreastSAM: A Study of Segment Anything Model for Breast Tumor Detection in Ultrasound Images

arXiv: https://arxiv.org/abs/2305.12447 · HTML: https://mlsanity.com/paper/2305.12447/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 22 references that are in the map.
- First posted on arXiv 21 May 2023.

## Builds on (most cited first, 11 of 11)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021)
- [nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation](https://mlsanity.com/paper/1809.10486/) (2018)
- [Polyp-SAM: Transfer SAM for Polyp Segmentation](https://mlsanity.com/paper/2305.00293/) (2023)
- [SkinSAM: Empowering Skin Cancer Segmentation with Segment Anything Model](https://mlsanity.com/paper/2304.13973/) (2023)
- [Advancing Medical Imaging with Language Models: A Journey from N-grams to ChatGPT](https://mlsanity.com/paper/2304.04920/) (2023)

## Built on by (most cited first, 7 of 7)

- [A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations](https://mlsanity.com/paper/2406.13844/) (2024)
- [Foundation Models for Biomedical Image Segmentation: A Survey](https://mlsanity.com/paper/2401.07654/) (2024)
- [Deep Interactive Segmentation of Medical Images: A Systematic Review and Taxonomy](https://mlsanity.com/paper/2311.13964/) (2023)
- [Segment Anything Model (SAM) for Radiation Oncology](https://mlsanity.com/paper/2306.11730/) (2023)
- [Sam2Rad: A Segmentation Model for Medical Images with Learnable Prompts](https://mlsanity.com/paper/2409.06821/) (2024)
- [Industrial Segment Anything -- a Case Study in Aircraft Manufacturing, Intralogistics, Maintenance, Repair, and Overhaul](https://mlsanity.com/paper/2307.12674/) (2023)
- [Attention-Driven Lightweight Model for Pigmented Skin Lesion Detection](https://mlsanity.com/paper/2308.02119/) (2023)

## Abstract (arXiv)

Breast cancer is one of the most common cancers among women worldwide, with early detection significantly increasing survival rates. Ultrasound imaging is a critical diagnostic tool that aids in early detection by providing real-time imaging of the breast tissue. We conducted a thorough investigation of the Segment Anything Model (SAM) for the task of interactive segmentation of breast tumors in ultrasound images. We explored three pre-trained model variants: ViT_h, ViT_l, and ViT_b, among which ViT_l demonstrated superior performance in terms of mean pixel accuracy, Dice score, and IoU score. The significance of prompt interaction in improving the model's segmentation performance was also highlighted, with substantial improvements in performance metrics when prompts were incorporated. The study further evaluated the model's differential performance in segmenting malignant and benign breast tumors, with the model showing exceptional proficiency in both categories, albeit with slightly better performance for benign tumors. Furthermore, we analyzed the impacts of various breast tumor characteristics - size, contrast, aspect ratio, and complexity - on segmentation performance. Our findings reveal that tumor contrast and size positively impact the segmentation result, while complex boundaries pose challenges. The study provides valuable insights for using SAM as a robust and effective algorithm for breast tumor segmentation in ultrasound images.