Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2103.00718v1
• Date: Mon, 1 Mar 2021 03:09:17 GMT
• Title: Autonomous Navigation of an Ultrasound Probe Towards Standard Scan Planes with Deep Reinforcement Learning
• Authors: Keyu Li, Jian Wang, Yangxin Xu, Hao Qin, Dongsheng Liu, Li Liu, Max Q.-H. Meng
• Abstract summary: We propose a framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine.
• Score: 28.17246919349759
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Autonomous ultrasound (US) acquisition is an important yet challenging task, as it involves interpretation of the highly complex and variable images and their spatial relationships. In this work, we propose a deep reinforcement learning framework to autonomously control the 6-D pose of a virtual US probe based on real-time image feedback to navigate towards the standard scan planes under the restrictions in real-world US scans. Furthermore, we propose a confidence-based approach to encode the optimization of image quality in the learning process. We validate our method in a simulation environment built with real-world data collected in the US imaging of the spine. Experimental results demonstrate that our method can perform reproducible US probe navigation towards the standard scan plane with an accuracy of $4.91mm/4.65^\circ$ in the intra-patient setting, and accomplish the task in the intra- and inter-patient settings with a success rate of $92\%$ and $46\%$, respectively. The results also show that the introduction of image quality optimization in our method can effectively improve the navigation performance.

Related papers

• Experimental Validation of Ultrasound Beamforming with End-to-End Deep Learning for Single Plane Wave Imaging [0.810120481608724]
  Ultrafast ultrasound imaging insonifies a medium with one or a combination of a few plane waves at different beam-steered angles instead of many focused waves. Deep learning approaches have been proposed to mitigate this disadvantage, in particular for single plane wave imaging. We consider data-to-image networks which incorporate a conventional image formation techniques as differentiable layers in the network architecture.
  arXiv  Detail & Related papers  (2024-04-22T13:58:36Z)
• Cardiac ultrasound simulation for autonomous ultrasound navigation [4.036497185262817]
  We propose a method to generate large amounts of ultrasound images from other modalities and from arbitrary positions. We present a novel simulation pipeline which uses segmentations from other modalities, an optimized data representation and GPU-accelerated Monte Carlo path tracing. The proposed approach allows for fast and accurate patient-specific ultrasound image generation, and its usability for training networks for navigation-related tasks is demonstrated.
  arXiv  Detail & Related papers  (2024-02-09T15:14:48Z)
• Localizing Scan Targets from Human Pose for Autonomous Lung Ultrasound Imaging [61.60067283680348]
  With the advent of COVID-19 global pandemic, there is a need to fully automate ultrasound imaging. We propose a vision-based, data driven method that incorporates learning-based computer vision techniques. Our method attains an accuracy level of 15.52 (9.47) mm for probe positioning and 4.32 (3.69)deg for probe orientation, with a success rate above 80% under an error threshold of 25mm for all scan targets.
  arXiv  Detail & Related papers  (2022-12-15T14:34:12Z)
• 6D Camera Relocalization in Visually Ambiguous Extreme Environments [79.68352435957266]
  We propose a novel method to reliably estimate the pose of a camera given a sequence of images acquired in extreme environments such as deep seas or extraterrestrial terrains. Our method achieves comparable performance with state-of-the-art methods on the indoor benchmark (7-Scenes dataset) using only 20% training data.
  arXiv  Detail & Related papers  (2022-07-13T16:40:02Z)
• Agent with Tangent-based Formulation and Anatomical Perception for Standard Plane Localization in 3D Ultrasound [56.7645826576439]
  We introduce a novel reinforcement learning framework for automatic SP localization in 3D US. First, we formulate SP localization in 3D US as a tangent-point-based problem in RL to restructure the action space. Second, we design an auxiliary task learning strategy to enhance the model's ability to recognize subtle differences crossing Non-SPs and SPs in plane search.
  arXiv  Detail & Related papers  (2022-07-01T14:53:27Z)
• VesNet-RL: Simulation-based Reinforcement Learning for Real-World US Probe Navigation [39.7566010845081]
  In freehand US examinations, sonographers often navigate a US probe to visualize standard examination planes with rich diagnostic information. We propose a simulation-based RL framework for real-world navigation of US probes towards the standard longitudinal views of vessels.
  arXiv  Detail & Related papers  (2022-05-10T09:34:42Z)
• Image-Guided Navigation of a Robotic Ultrasound Probe for Autonomous Spinal Sonography Using a Shadow-aware Dual-Agent Framework [35.17207004351791]
  We propose a novel dual-agent framework that integrates a reinforcement learning agent and a deep learning agent. Our method can effectively interpret the US images and navigate the probe to acquire multiple standard views of the spine.
  arXiv  Detail & Related papers  (2021-11-03T12:11:27Z)
• Deep Learning for Ultrasound Beamforming [120.12255978513912]
  Beamforming, the process of mapping received ultrasound echoes to the spatial image domain, lies at the heart of the ultrasound image formation chain. Modern ultrasound imaging leans heavily on innovations in powerful digital receive channel processing. Deep learning methods can play a compelling role in the digital beamforming pipeline.
  arXiv  Detail & Related papers  (2021-09-23T15:15:21Z)
• Homography augumented momentum constrastive learning for SAR image retrieval [3.9743795764085545]
  We propose a deep learning-based image retrieval approach using homography transformation augmented contrastive learning. We also propose a training method for the DNNs induced by contrastive learning that does not require any labeling procedure.
  arXiv  Detail & Related papers  (2021-09-21T17:27:07Z)
• A parameter refinement method for Ptychography based on Deep Learning concepts [55.41644538483948]
  coarse parametrisation in propagation distance, position errors and partial coherence frequently menaces the experiment viability. A modern Deep Learning framework is used to correct autonomously the setup incoherences, thus improving the quality of a ptychography reconstruction. We tested our system on both synthetic datasets and also on real data acquired at the TwinMic beamline of the Elettra synchrotron facility.
  arXiv  Detail & Related papers  (2021-05-18T10:15:17Z)
• Spatio-Temporal Deep Learning Methods for Motion Estimation Using 4D OCT Image Data [63.73263986460191]
  Localizing structures and estimating the motion of a specific target region are common problems for navigation during surgical interventions. We investigate whether using a temporal stream of OCT image volumes can improve deep learning-based motion estimation performance. Using 4D information for the model input improves performance while maintaining reasonable inference times.
  arXiv  Detail & Related papers  (2020-04-21T15:43:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.