ArticleAnnals of biomedical engineering2023
Automatic Segmentation of the Left Atrium from Computed Tomography Angiography Images.
Article in Annals of biomedical engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 5 citations in OpenAlex.
- Cardiac CT Roadmap for Left Atrial Appendage Occlusion: From Planning to Strategy to Follow-Up.Journal of clinical medicine · 2026Review
- ABR-UNet3D: Aspect-Aware Boundary-Resilient Attention for Robust Cardiac MRI Segmentation.Diagnostics (Basel, Switzerland) · 2026Article
- A fully automated explainable predictive model for diagnosing pre-capillary and post-capillary pulmonary hypertension on routine unenhanced CT: results from the ASPIRE registry.European heart journal. Digital health · 2026Article
- Sensitivity of Left Atrial Flow Dynamics to Echocardiographic and Computed Tomography Data.Journal of cardiovascular translational research · 2025Article
- Automatic quantification of left atrium volume for cardiac rhythm analysis leveraging 3D residual UNet for time-varying segmentation of ECG-gated CT.Computational and structural biotechnology journal · 2025Article
- Accurate fully automated assessment of left ventricle, left atrium, and left atrial appendage function from computed tomography using deep learning.European heart journal. Imaging methods and practice · 2024Article
- Advancements in cardiac structures segmentation: a comprehensive systematic review of deep learning in CT imaging.Frontiers in cardiovascular medicine · 2024Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The left atrial appendage (LAA) causes 91% of thrombi in atrial fibrillation patients, a potential harbinger of stroke. Leveraging computed tomography angiography (CTA) images, radiologists interpret the left atrium (LA) and LAA geometries to stratify stroke risk. Nevertheless, accurate LA segmentation remains a time-consuming task with high inter-observer variability. Binary masks of the LA and their corresponding CTA images were used to train and test a 3D U-Net to automate LA segmentation. One model was trained using the entire unified-image-volume while a second model was trained on regional patch-volumes which were run for inference and then assimilated back into the full volume. The unified-image-volume U-Net achieved median DSCs of 0.92 and 0.88 for the train and test sets, respectively; the patch-volume U-Net achieved median DSCs of 0.90 and 0.89 for the train and test sets, respectively. This indicates that the unified-image-volume and patch-volume U-Net models captured up to 88 and 89% of the LA/LAA boundary's regional complexity, respectively. Additionally, the results indicate that the LA/LAA were fully captured in most of the predicted segmentations. By automating the segmentation process, our deep learning model can expedite LA/LAA shape, informing stratification of stroke risk.
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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.