100 research outputs found
Relational Modeling for Robust and Efficient Pulmonary Lobe Segmentation in CT Scans
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
Emphysema Subtyping on Thoracic Computed Tomography Scans using Deep Neural Networks
Accurate identification of emphysema subtypes and severity is crucial for
effective management of COPD and the study of disease heterogeneity. Manual
analysis of emphysema subtypes and severity is laborious and subjective. To
address this challenge, we present a deep learning-based approach for
automating the Fleischner Society's visual score system for emphysema subtyping
and severity analysis. We trained and evaluated our algorithm using 9650
subjects from the COPDGene study. Our algorithm achieved the predictive
accuracy at 52\%, outperforming a previously published method's accuracy of
45\%. In addition, the agreement between the predicted scores of our method and
the visual scores was good, where the previous method obtained only moderate
agreement. Our approach employs a regression training strategy to generate
categorical labels while simultaneously producing high-resolution localized
activation maps for visualizing the network predictions. By leveraging these
dense activation maps, our method possesses the capability to compute the
percentage of emphysema involvement per lung in addition to categorical
severity scores. Furthermore, the proposed method extends its predictive
capabilities beyond centrilobular emphysema to include paraseptal emphysema
subtypes
Emphysema subtyping on thoracic computed tomography scans using deep neural networks
Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p
Emphysema subtyping on thoracic computed tomography scans using deep neural networks
Accurate identification of emphysema subtypes and severity is crucial for effective management of COPD and the study of disease heterogeneity. Manual analysis of emphysema subtypes and severity is laborious and subjective. To address this challenge, we present a deep learning-based approach for automating the Fleischner Society’s visual score system for emphysema subtyping and severity analysis. We trained and evaluated our algorithm using 9650 subjects from the COPDGene study. Our algorithm achieved the predictive accuracy at 52%, outperforming a previously published method’s accuracy of 45%. In addition, the agreement between the predicted scores of our method and the visual scores was good, where the previous method obtained only moderate agreement. Our approach employs a regression training strategy to generate categorical labels while simultaneously producing high-resolution localized activation maps for visualizing the network predictions. By leveraging these dense activation maps, our method possesses the capability to compute the percentage of emphysema involvement per lung in addition to categorical severity scores. Furthermore, the proposed method extends its predictive capabilities beyond centrilobular emphysema to include paraseptal emphysema subtypes.</p
Over-expression of eukaryotic translation initiation factor 4 gamma 1 correlates with tumor progression and poor prognosis in nasopharyngeal carcinoma
<p>Abstract</p> <p>Background</p> <p>The aim of the present study was to analyze the expression of eukaryotic translation initiation factor 4 gamma 1 (<it>EIF4G1</it>) in nasopharyngeal carcinoma (NPC) and its correlation with clinicopathologic features, including patients' survival time.</p> <p>Methods</p> <p>Using real-time PCR, we detected the expression of <it>EIF4G1 </it>in normal nasopharyngeal tissues, immortalized nasopharyngeal epithelial cell lines NP69, NPC tissues and cell lines. <it>EIF4G1 </it>protein expression in NPC tissues was examined using immunohistochemistry. Survival analysis was performed using Kaplan-Meier method. The effect of <it>EIF4G1 </it>on cell invasion and tumorigenesis were investigated.</p> <p>Results</p> <p>The expression levels of <it>EIF4G1 </it>mRNA were significantly greater in NPC tissues and cell lines than those in the normal nasopharyngeal tissues and NP69 cells (<it>P </it>< 0.001). Immunohistochemical analysis revealed that the expression of <it>EIF4G1 </it>protein was higher in NPC tissues than that in the nasopharyngeal tissues (<it>P </it>< 0.001). In addition, the levels of <it>EIF4G1 </it>protein in tumors were positively correlated with tumor T classification (<it>P </it>= 0.039), lymph node involvement (N classification, <it>P </it>= 0.008), and the clinical stages (<it>P </it>= 0.003) of NPC patients. Patients with higher <it>EIF4G</it>1 expression had shorter overall survival time (<it>P </it>= 0.019). Multivariate analysis showed that <it>EIF4G1 </it>expression was an independent prognostic indicator for the overall survival of NPC patients. Using shRNA to knock down the expression of <it>EIF4G1 </it>not only markedly inhibited cell cycle progression, proliferation, migration, invasion, and colony formation, but also dramatically suppressed <it>in vivo </it>xenograft tumor growth.</p> <p>Conclusion</p> <p>Our data suggest that <it>EIF4G1 </it>can serve as a biomarker for the prognosis of NPC patients.</p
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