This code accompanies the paper "A Fully Autonomous Robotic Ultrasound System for Thyroid Scanning".
The current thyroid ultrasound relies heavily on the experience and skills of the sonographer and the expertise of the radiologist, and the process is physically and cognitively exhausting. In this paper, we report a novel fully autonomous robotic ultrasound system (FARUS), which is able to scan thyroid regions without human assistance and identify malignant nodules. In this system, human skeleton point recognition, reinforcement learning, and force feedback are used to deal with the difficulties in locating thyroid targets. The orientation of the ultrasound probe is adjusted dynamically via Bayesian optimization. Experimental results on volunteering participants demonstrated that FARUS can perform high-quality ultrasound scans, close to manual scans obtained by clinicians, may detect thyroid nodules and can provide data on nodule characteristics for ACR TI-RADS calculation.
Python 3.8.15
This code requirements are stated at requirements.txt
With the approval of the ethical review committee, we recruited of several groups of volunteers. Firstly, we employed FARUS system to autonomously scan 70 volunteers (SCUTG6K). To address the limitations of handheld US equipment in accurately diagnosing nodules, we opted to employ portable US equipment to gather additional sets of data (SCUTG2K).
SCUTG8K can be obtained via https://drive.google.com/drive/folders/1z-n69dk_ANT3ZstAhpjBA2SFz2DrxEmM?usp=drive_link
dependecy requirements: gym: v_0.21.0 stable_baselines3: v ray
Training: run thyroid_glanod_dqn_cjp_train.py file
Predict: run thyroid_glanod_dqn_predict_patient.py file
If you find our work useful or our work gives you any insights, please cite: Su, K., Liu, J., Ren, X. et al. A fully autonomous robotic ultrasound system for thyroid scanning. Nat Commun 15, 4004 (2024). https://doi.org/10.1038/s41467-024-48421-y
MIT License
Copyright (c) 2023 Kang Su
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