ArticleMedical physics2021
Deep learning-based motion tracking using ultrasound images.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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10 citing papers in PubMed, 26 citations in OpenAlex.
- Artificial intelligence for treatment delivery: image-guided radiotherapy.Strahlentherapie und Onkologie : Organ der Deutschen Rontgengesellschaft ... [et al] · 2025Review
- Robust unsupervised texture segmentation for motion analysis in ultrasound images.International journal of computer assisted radiology and surgery · 2025Article
- Article
- In vivo Multi-perspective 3D + t Ultrasound Imaging and Motion Estimation of Abdominal Aortic Aneurysms.Ultrasonic imaging · 2025Article
- Artificial intelligence-based motion tracking in cancer radiotherapy: A review.Journal of applied clinical medical physics · 2024Review
- Landmark tracking in liver US images using cascade convolutional neural networks with long short-term memory.Measurement science & technology · 2023Article
- Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation.Sensors (Basel, Switzerland) · 2023Article
- Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.Medical image analysis · 2023Article
- Therapeutic Ultrasound Effects on Human Induced Pluripotent Stem Cell Cardiomyocytes Measured Optically and with Spectral Ultrasound.Ultrasound in medicine & biology · 2022Article
- A Feasibility Study of Hybrid Deep-Learning Prediction with Online Adaptation of Breathing Irregularities for Long-Term Internal Organ Motion During Radiotherapy.Technology in cancer research & treatmentArticle
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Authors and funding
8 authors at 3 institutions in 1 country.
Funding
Abstract
purposeUltrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It has the potential for intra-fraction motion tracking in radiation therapy. In this study, a deep learning-based method has been developed to tackle the challenges in motion tracking using US imaging.
methodsWe present a Markov-like network, which is implemented via generative adversarial networks, to extract features from sequential US frames (one tracked frame followed by untracked frames) and thereby estimate a set of deformation vector fields (DVFs) through the registration of the tracked frame and the untracked frames. The positions of the landmarks in the untracked frames are finally determined by shifting landmarks in the tracked frame according to the estimated DVFs. The performance of the proposed method was evaluated on the testing dataset by calculating the tracking error (TE) between the predicted and ground truth landmarks on each frame.
resultsThe proposed method was evaluated using the MICCAI CLUST 2015 dataset which was collected using seven US scanners with eight types of transducers and the Cardiac Acquisitions for Multi-structure Ultrasound Segmentation (CAMUS) dataset which was acquired using GE Vivid E95 ultrasound scanners. The CLUST dataset contains 63 2D and 22 3D US image sequences respectively from 42 and 18 subjects, and the CAMUS dataset includes 2D US images from 450 patients. On CLUST dataset, our proposed method achieved a mean tracking error of 0.70 ± 0.38 mm for the 2D sequences and 1.71 ± 0.84 mm for the 3D sequences for those public available annotations. And on CAMUS dataset, a mean tracking error of 0.54 ± 1.24 mm for the landmarks in the left atrium was achieved.
conclusionsA novel motion tracking algorithm using US images based on modern deep learning techniques has been demonstrated in this study. The proposed method can offer millimeter-level tumor motion prediction in real time, which has the potential to be adopted into routine tumor motion management in radiation therapy.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.