bibtext: @InProceedings{ Ama_Goalconditioned_MICCAI2024,
                   author = { Amadou, Abdoul Aziz and Singh, Vivek and Ghesu, Florin C. and Kim, Young-Ho and Stanciulescu, Laura and Sai, Harshitha P. and Sharma, Puneet and Young, Alistair and Rajani, Ronak and Rhode, Kawal },
                   title = { { Goal-conditioned reinforcement learning for ultrasound navigation guidance } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15011 },
                   month = {October},
                   pages = { pending },
                }
abstract: Transesophageal echocardiography (TEE) plays a pivotal role in cardiology for diagnostic and interventional procedures. However, using it effectively requires extensive training due to the intricate nature of image acquisition and interpretation. To enhance the efficiency of novice sonographers and reduce variability in scan acquisitions, we propose a novel ultrasound (US) navigation assistance method based on contrastive learning as goal-conditioned reinforcement learning (GCRL). We augment the previous framework using a novel contrastive patient batching method (CPB) and a data-augmented contrastive loss, both of which we demonstrate are essential to ensure generalization to anatomical variations across patients. The proposed framework enables navigation to both standard diagnostic as well as intricate interventional views with a single model. Our method was developed with a large dataset of 789 patients and obtained an average error of 6.56 mm in position and 9.36 degrees in angle on a testing dataset of 140 patients, which is competitive or superior to models trained on individual views. Furthermore, we quantitatively validate our method’s ability to navigate to interventional views such as the Left Atrial Appendage (LAA) view used in LAA closure. Our approach holds promise in providing valuable guidance during transesophageal ultrasound examinations, contributing to the advancement of skill acquisition for cardiac ultrasound practitioners.
title: Goal-conditioned reinforcement learning for ultrasound navigation guidance
authors: Amadou, Abdoul Aziz Singh, Vivek Ghesu, Florin C. Kim, Young-Ho Stanciulescu, Laura Sai, Harshitha P. Sharma, Puneet Young, Alistair Rajani, Ronak Rhode, Kawal