ArticleQuantitative imaging in medicine and surgery2021
Fully automatic deep learning trained on limited data for carotid artery segmentation from large image volumes.
Article in Quantitative imaging in medicine and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Computer vision applications in vascular surgery: a systematic review and critical appraisal.NPJ digital medicine · 2026Article
- Pericarotid Fat as a Marker of Cerebrovascular Risk.AJNR. American journal of neuroradiology · 2024Review
- Automatic detection of posterior superior alveolar artery in dental cone-beam CT images using a deeply supervised multi-scale 3D network.Dento maxillo facial radiology · 2024Article
- A deep learning model for carotid plaques detection based on CTA images: a two stepwise early-stage clinical validation study.Frontiers in neurology · 2024Article
- PSAA-nnUNet: An Efficient Method for CT Carotid Artery Image Segmentation.Advances in experimental medicine and biology · 2024Article
- The development and validation of pathological sections based U-Net deep learning segmentation model for the detection of esophageal mucosa and squamous cell neoplasm.Journal of gastrointestinal oncology · 2023Article
- Group theoretic particle swarm optimization for multi-level threshold lung cancer image segmentation.Quantitative imaging in medicine and surgery · 2023Article
- Mycobacterial cavity on chest computed tomography: clinical implications and deep learning-based automatic detection with quantification.Quantitative imaging in medicine and surgery · 2023Article
- A new potential risk factor for permanent cranial nerve injury following carotid body tumor resection.Quantitative imaging in medicine and surgery · 2023Article
- Subset selection strategy-based pancreas segmentation in CT.Quantitative imaging in medicine and surgery · 2022Article
- Ability of weakly supervised learning to detect acute ischemic stroke and hemorrhagic infarction lesions with diffusion-weighted imaging.Quantitative imaging in medicine and surgery · 2022Article
- The application of the nnU-Net-based automatic segmentation model in assisting carotid artery stenosis and carotid atherosclerotic plaque evaluation.Frontiers in physiology · 2022Article
- Clot burden of acute pulmonary thromboembolism: comparison of two deep learning algorithms, Qanadli score, and Mastora score.Quantitative imaging in medicine and surgery · 2022Article
- Back Propagation Neural Network-Based Magnetic Resonance Imaging Image Features in Treating Intestinal Obstruction in Digestive Tract Diseases with Chengqi Decoction.Contrast media & molecular imaging · 2021Article
- Observer Variability in CT Angiography Carotid Segmentation: Assessing Variability to Set Minimum Clinical Performance.Journal of neuroimaging : official journal of the American Society of NeuroimagingArticle
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5 authors.
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Abstract
backgroundThe objectives of this study were to develop a 3D convolutional deep learning framework (CarotidNet) for fully automatic segmentation of carotid bifurcations in computed tomography angiography (CTA) images and to facilitate the quantification of carotid stenosis and risk assessment of stroke.
methodsOur pipeline was a two-stage cascade network that included a localization phase and a segmentation phase. The network framework was based on the 3D version of U-Net, but was refined in three ways: (I) by adding residual connections and a deep supervision strategy to cope with the vanishing problem in back-propagation; (II) by adopting dilated convolution in order to strengthen the capacity to capture contextual information; and (III) by establishing a hybrid objective function to address the extreme imbalance between foreground and background voxels.
resultsWe trained our networks on 15 cases and evaluated their performance based on 41 cases from the MICCAI Challenge 2009 dataset. A Dice similarity coefficient of 82.3% was achieved for the test cases.
conclusionsWe developed a carotid segmentation method based on U-Net that can segment tiny carotid bifurcation lumens from very large backgrounds with no manual intervention. This was the first attempt to use deep learning to achieve carotid bifurcation segmentation in 3D CTA images. Our results indicate that deep learning is a promising method for automatically extracting carotid bifurcation lumens.
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