ArticleMedical physics2019
Fully automatic segmentation of 4D MRI for cardiac functional measurements.
Article in Medical physics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 31 citations in OpenAlex.
- Applications of mixed reality with medical imaging for training and clinical practice.Journal of medical imaging (Bellingham, Wash.) · 2024Review
- DSMRI: Domain Shift Analyzer for Multi-Center MRI Datasets.Diagnostics (Basel, Switzerland) · 2023Article
- Contrast-enhanced CT radiomics improves the prediction of abdominal aortic aneurysm progression.European radiology · 2023Article
- HEARTBEAT4D: An Open-source Toolbox for Turning 4D Cardiac CT into VR/AR.Journal of digital imaging · 2022Article
- AutoComBat: a generic method for harmonizing MRI-based radiomic features.Scientific reports · 2022Article
- Deep learning based fully automatic segmentation of the left ventricular endocardium and epicardium from cardiac cine MRI.Quantitative imaging in medicine and surgery · 2021Article
- Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study.Journal of medical Internet research · 2021Article
- Machine Learning Driven Contouring of High-Frequency Four-Dimensional Cardiac Ultrasound Data.Applied sciences (Basel, Switzerland) · 2021Article
- Fetal Cerebral Oxygenation Is Impaired in Congenital Heart Disease and Shows Variable Response to Maternal Hyperoxia.Journal of the American Heart Association · 2021Article
- Article
- Reference Ranges for Left Ventricular Curvedness and Curvedness-Based Functional Indices Using Cardiovascular Magnetic Resonance in Healthy Asian Subjects.Scientific reports · 2020Article
- Article
- Extending Cardiac Functional Assessment with Respiratory-Resolved 3D Cine MRI.Scientific reports · 2019Article
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Authors and funding
9 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeSegmentation of cardiac medical images, an important step in measuring cardiac function, is usually performed either manually or semiautomatically. Fully automatic segmentation of the left ventricle (LV), the right ventricle (RV) as well as the myocardium of three-dimensional (3D) magnetic resonance (MR) images throughout the entire cardiac cycle (four-dimensional, 4D), remains challenging. This study proposes a deformable-based segmentation methodology for efficiently segmenting 4D (3D + t) cardiac MR images.
methodsThe proposed methodology first used the Hough transform and the local Gaussian distribution method (LGD) to segment the LV endocardial contours from cardiac MR images. Following this, a novel level set-based shape prior method was applied to generate the LV epicardial contours and the RV boundary.
resultsThis automatic image segmentation approach has been applied to studies on 17 subjects. The results demonstrated that the proposed method was efficient compared to manual segmentation, achieving a segmentation accuracy with average Dice values of 88.62 ± 5.47%, 87.35 ± 7.26%, and 82.63 ± 6.22% for the LV endocardial, LV epicardial, and RV contours, respectively.
conclusionsWe have presented a method for accurate LV and RV segmentation. Compared to three existing methods, the proposed method can successfully segment the LV and yield the highest Dice value. This makes it an option for clinical assessment of the volume, size, and thickness of the ventricles.
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