# BUSIS: A Benchmark for Breast Ultrasound Image Segmentation

arXiv: https://arxiv.org/abs/1801.03182 · HTML: https://mlsanity.com/paper/1801.03182/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jan 2018.

## Built on by (most cited first, 30 of 32)

- [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023)
- [Federated Learning for Medical Image Analysis: A Survey](https://mlsanity.com/paper/2306.05980/) (2023)
- [ScribblePrompt: Fast and Flexible Interactive Segmentation for Any Biomedical Image](https://mlsanity.com/paper/2312.07381/) (2023)
- [CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion](https://mlsanity.com/paper/2308.01239/) (2023)
- [FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation](https://mlsanity.com/paper/2205.01840/) (2022)
- [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024)
- [Transformers in Healthcare: A Survey](https://mlsanity.com/paper/2307.00067/) (2023)
- [Rethinking the Unpretentious U-net for Medical Ultrasound Image Segmentation](https://mlsanity.com/paper/2209.07193/) (2022)
- [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024)
- [Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions](https://mlsanity.com/paper/2304.06662/) (2023)
- [Recent Progress in Transformer-based Medical Image Analysis](https://mlsanity.com/paper/2208.06643/) (2022)
- [Multi-Level Global Context Cross Consistency Model for Semi-Supervised Ultrasound Image Segmentation with Diffusion Model](https://mlsanity.com/paper/2305.09447/) (2023)
- [Boosting Breast Ultrasound Video Classification by the Guidance of Keyframe Feature Centers](https://mlsanity.com/paper/2306.06877/) (2023)
- [MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis](https://mlsanity.com/paper/2507.00377/) (2025)
- [Breast Ultrasound Tumor Classification Using a Hybrid Multitask CNN-Transformer Network](https://mlsanity.com/paper/2308.02101/) (2023)
- [MobileUtr: Revisiting the relationship between light-weight CNN and Transformer for efficient medical image segmentation](https://mlsanity.com/paper/2312.01740/) (2023)
- [SRSNetwork: Siamese Reconstruction-Segmentation Networks based on Dynamic-Parameter Convolution](https://mlsanity.com/paper/2312.01741/) (2023)
- [UniUSNet: A Promptable Framework for Universal Ultrasound Disease Prediction and Tissue Segmentation](https://mlsanity.com/paper/2406.01154/) (2024)
- [Do Sharpness-based Optimizers Improve Generalization in Medical Image Analysis?](https://mlsanity.com/paper/2408.04065/) (2024)
- [XBusNet: Text-Guided Breast Ultrasound Segmentation via Multimodal Vision-Language Learning](https://mlsanity.com/paper/2509.07213/) (2025)
- [UMind-VL: A Generalist Ultrasound Vision-Language Model for Unified Grounded Perception and Comprehensive Interpretation](https://mlsanity.com/paper/2511.22256/) (2025)
- [MIRST-DM: Multi-Instance RST with Drop-Max Layer for Robust Classification of Breast Cancer](https://mlsanity.com/paper/2205.01674/) (2022)
- [CARE: Anti-entanglement Ultrasound Image Segmentation via Channel-Aware Region Extrication](https://mlsanity.com/paper/2508.13899/) (2025)
- [LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion](https://mlsanity.com/paper/2508.15476/) (2025)
- [Improving Segmentation of Breast Ultrasound Images: Semi Automatic Two Pointers Histogram Splitting Technique](https://mlsanity.com/paper/2210.10975/) (2022)
- [A2DMN: Anatomy-Aware Dilated Multiscale Network for Breast Ultrasound Semantic Segmentation](https://mlsanity.com/paper/2403.15560/) (2024)
- [UltraUPConvNet: A UPerNet- and ConvNeXt-Based Multi-Task Network for Ultrasound Tissue Segmentation and Disease Prediction](https://mlsanity.com/paper/2509.11108/) (2025)
- [BUSTR: Descriptor-Aware Vision-Language Learning for Breast Ultrasound Report Generation](https://mlsanity.com/paper/2511.20956/) (2025)
- [Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains](https://mlsanity.com/paper/2512.13534/) (2025)
- [Diagnostic Performance of Universal-Learning Ultrasound AI Across Multiple Organs and Tasks: the UUSIC25 Challenge](https://mlsanity.com/paper/2512.17279/) (2025)

## Abstract (arXiv)

Breast ultrasound (BUS) image segmentation is challenging and critical for BUS Comput-er-Aided Diagnosis (CAD) systems. Many BUS segmentation approaches have been studied in the last two decades, but the performances of most approaches have been assessed using relatively small private datasets with different quantitative metrics, which results in a discrepancy in performance comparison. Therefore, there is a pressing need for building a benchmark to compare existing methods using a public dataset objectively, to determine the performance of the best breast tumor segmentation algorithm available today, and to investigate what segmentation strategies are valuable in clinical practice and theoretical study. In this work, a benchmark for B-mode breast ultrasound image segmentation is presented. In the benchmark, 1) we collected 562 breast ultrasound images, prepared a software tool, and involved four radiologists in obtaining accurate annotations through standardized procedures; 2) we extensively compared the performance of sixteen state-of-the-art segmentation methods and discussed their advantages and disadvantages; 3) we proposed a set of valuable quantitative metrics to evaluate both semi-automatic and fully automatic segmentation approaches; and 4) the successful segmentation strategies and possible future improvements are discussed in details.