Unified Ultrasound Intelligence Toward an End-to-End Agentic System

Official PyTorch implementation of USTri
A tri-stage ultrasound intelligence pipeline for multi-organ, multi-task, and workflow-level ultrasound analysis.

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✨ Highlights

• Tri-stage design: from a universal generalist (USGen) to lightweight specialists (USpec) and finally an agentic system (USAgent).
• Unified task support: segmentation, classification, detection, and regression in one framework.
• Workflow-level inference: compose trained specialists into interpretable, clinician-style pipelines.
• Strong performance on the FMC UIA validation benchmark across 27 datasets and 4 task types.

🧠 Overview

USTri is designed for real-world ultrasound intelligence, where data vary widely across organs, views, devices, and acquisition protocols.

It contains three stages:

• Stage I — USGen: a universal ultrasound generalist trained with dataset rotation to learn broad and transferable priors.
• Stage II — USpec: lightweight dataset-specific specialization with frozen backbone + compact heads.
• Stage III — USAgent: an agentic inference layer that routes tasks, calls specialists as tools, and renders structured reports.

📊 Main Results

On the FMC UIA unseen-domain validation set, USpec achieves the best overall performance reported in the paper:

Task	Metric	USpec
Segmentation	DSC ↑	0.8980
Segmentation	HD ↓	27.21
Classification	AUC ↑	0.9352
Classification	F1 ↑	0.8593
Classification	MCC ↑	0.7675
Detection	IoU ↑	0.8000
Regression	MRE ↓	18.42

🚀 Getting Started

1) Clone the repository

git clone https://github.com/MacDunno/USTri.git
cd USTri

2) Install dependencies

Create your environment and install the packages used in the codebase:

pip install torch torchvision torchaudio
pip install albumentations opencv-python pandas numpy tqdm scikit-learn SimpleITK
pip install tensorboard segmentation-models-pytorch

3) Prepare the dataset

The training code expects the dataset under:

data/train/

Please organize the data following the official FMC UIA format, including the CSV metadata files used by the provided scripts.

🏋️ Training

Stage I — train the universal generalist

python train.py

Stage II — finetune a specialist for one dataset

python train_phase2_single_dataset.py --task-id IUGC

You can replace IUGC with any supported task id defined in model_factory.py.

📈 Evaluation

Run evaluation on saved predictions with:

python evaluate.py

🔍 Supported Task Types

USTri supports four unified task categories:

• Segmentation
• Classification
• Detection
• Regression

Example task identifiers in the current codebase include:

• IUGC
• FUGC
• fetal_femur
• breast_2cls
• breast_3cls
• fetal_plane_cls
• fetal_head_pos_cls
• fetal_sacral_pos_cls

🧾 Citation

If you find this repository useful, please cite:

@inproceedings{ustri,
  title     = {Unified Ultrasound Intelligence Toward an End-to-End Agentic System},
  author    = {Ma, Chen and Li, Yunshu and Fu, Junhu and Liang, Shuyu and Wang, Yuanyuan and Guo, Yi},
  booktitle = {IEEE International Symposium on Biomedical Imaging (ISBI)},
  year      = {2026}
}

@article{tinyusfm,
  author={Ma, Chen and Jiao, Jing and Liang, Shuyu and Fu, Junhu and Wang, Qin and Li, Zeju and Wang, Yuanyuan and Guo, Yi},
  journal={IEEE Journal of Biomedical and Health Informatics}, 
  title={TinyUSFM: Towards Compact and Efficient Ultrasound Foundation Models}, 
  year={2026},
  pages={1-14},
  doi={10.1109/JBHI.2026.3678309}
}

@incollection{iugc,
  title={Unlabeled Data-Driven Fetal Landmark Detection in Intrapartum Ultrasound},
  author={Ma, Chen and Li, Yunshu and Guo, Bowen and Jiao, Jing and Huang, Yi and Wang, Yuanyuan and Guo, Yi},
  booktitle={Intrapartum Ultrasound Grand Challenge},
  pages={14--23},
  year={2025},
  publisher={Springer}
}