ArticleIEEE transactions on medical imaging2020
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers.
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44 citing papers in PubMed, 101 citations in OpenAlex.
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- Dense regression activation maps for lesion segmentation in CT scans of COVID-19 patients.Medical image analysis · 2023Article
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- Quantification of pulmonary involvement in COVID-19 pneumonia: an upgrade of theEuropean physical journal plus · 2023Article
- Latent class analysis of imaging and clinical respiratory parameters from patients with COVID-19-related ARDS identifies recruitment subphenotypes.Critical care (London, England) · 2022Article
- HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.Computers in biology and medicine · 2022Article
- Explainable multi-instance and multi-task learning for COVID-19 diagnosis and lesion segmentation in CT images.Knowledge-based systems · 2022Article
- FCF: Feature complement fusion network for detecting COVID-19 through CT scan images.Applied soft computing · 2022Article
- Automated estimation of total lung volume using chest radiographs and deep learning.Medical physics · 2022Article
- Automated COVID-19 Grading With Convolutional Neural Networks in Computed Tomography Scans: A Systematic Comparison.IEEE transactions on artificial intelligence · 2022Article
- Semi-Supervised Segmentation of Radiation-Induced Pulmonary Fibrosis From Lung CT Scans With Multi-Scale Guided Dense Attention.IEEE transactions on medical imaging · 2022Article
- Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.International journal of computer assisted radiology and surgery · 2022Article
- Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks.Computers in biology and medicine · 2022Review
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4 authors.
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Abstract
Pulmonary lobe segmentation in computed tomography scans is essential for regional assessment of pulmonary diseases. Recent works based on convolution neural networks have achieved good performance for this task. However, they are still limited in capturing structured relationships due to the nature of convolution. The shape of the pulmonary lobes affect each other and their borders relate to the appearance of other structures, such as vessels, airways, and the pleural wall. We argue that such structural relationships play a critical role in the accurate delineation of pulmonary lobes when the lungs are affected by diseases such as COVID-19 or COPD. In this paper, we propose a relational approach (RTSU-Net) that leverages structured relationships by introducing a novel non-local neural network module. The proposed module learns both visual and geometric relationships among all convolution features to produce self-attention weights. With a limited amount of training data available from COVID-19 subjects, we initially train and validate RTSU-Net on a cohort of 5000 subjects from the COPDGene study (4000 for training and 1000 for evaluation). Using models pre-trained on COPDGene, we apply transfer learning to retrain and evaluate RTSU-Net on 470 COVID-19 suspects (370 for retraining and 100 for evaluation). Experimental results show that RTSU-Net outperforms three baselines and performs robustly on cases with severe lung infection due to COVID-19.
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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.