ArticleMedical image analysis2018
A deep Boltzmann machine-driven level set method for heart motion tracking using cine MRI images.
Article in Medical image analysis, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Development and validation of a deep reinforcement learning algorithm for auto-delineation of organs at risk in cervical cancer radiotherapy.Scientific reports · 2025Article
- Inter-fractional portability of deep learning models for lung target tracking on cine imaging acquired in MRI-guided radiotherapy.Physical and engineering sciences in medicine · 2024Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Co-attention spatial transformer network for unsupervised motion tracking and cardiac strain analysis in 3D echocardiography.Medical image analysis · 2023Article
- Deformable cardiac surface tracking by adaptive estimation algorithms.Scientific reports · 2023Article
- Machine intelligence in non-invasive endocrine cancer diagnostics.Nature reviews. Endocrinology · 2022Review
- Learning-based Cancer Treatment Outcome Prognosis using Multimodal Biomarkers.IEEE transactions on radiation and plasma medical sciences · 2022Article
- Cardiac MRI segmentation of the atria based on UU-NET.Frontiers in cardiovascular medicine · 2022Article
- [A review on motion tracking methods for left myocardium based on cardiac cine magnetic resonance image].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2020Review
- An integrated multi-objective whale optimized support vector machine and local texture feature model for severity prediction in subjects with cardiovascular disorder.International journal of computer assisted radiology and surgery · 2020Article
- An introduction to deep learning in medical physics: advantages, potential, and challenges.Physics in medicine and biology · 2020Review
- Deep Learning Intervention for Health Care Challenges: Some Biomedical Domain Considerations.JMIR mHealth and uHealth · 2019Article
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Authors and funding
13 authors.
Funding
Abstract
Heart motion tracking for radiation therapy treatment planning can result in effective motion management strategies to minimize radiation-induced cardiotoxicity. However, automatic heart motion tracking is challenging due to factors that include the complex spatial relationship between the heart and its neighboring structures, dynamic changes in heart shape, and limited image contrast, resolution, and volume coverage. In this study, we developed and evaluated a deep generative shape model-driven level set method to address these challenges. The proposed heart motion tracking method makes use of a heart shape model that characterizes the statistical variations in heart shapes present in a training data set. This heart shape model was established by training a three-layered deep Boltzmann machine (DBM) in order to characterize both local and global heart shape variations. During the tracking phase, a distance regularized level-set evolution (DRLSE) method was applied to delineate the heart contour on each frame of a cine MRI image sequence. The trained shape model was embedded into the DRLSE method as a shape prior term to constrain an evolutional shape to reach the desired heart boundary. Frame-by-frame heart motion tracking was achieved by iteratively mapping the obtained heart contour for each frame to the next frame as a reliable initialization, and performing a level-set evolution. The performance of the proposed motion tracking method was demonstrated using thirty-eight coronal cine MRI image sequences.
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