2D versus 3D artificial intelligence-driven segmentations of airway alterations in cystic fibrosis: which one is better?
Hadj Bouzid, I.; Benlala, i.; Dournes, G.
Show abstract
Purpose or Learning ObjectiveArtificial intelligence (AI) with convolutional neural network allows fully automated detection and segmentation of bronchial changes on CT-scans of cystic fibrosis (CF). However, the superiority of two-dimensional (2D) versus three-dimensional (3D) architectures remains to be explored. Method or BackgroundCT-scans from fifty CF patients were retrospectively included at two CF reference centers. The nnUnet model was implemented in both 2D and 3D, and trained to segment five structural alterations: bronchiectasis, wall thickening, mucus plugs, bronchiolar impactions and consolidations. A semantic validation was done by using fifty CTs with a five-fold cross validation strategy, by comparing normalized Dice-Sorensen coefficient (DSC) between 2D and 3D architectures, with manual segmentations as Gold Standard. Results or FindingsThe 3D nnUnet was found able to segment the five CF main hallmarks such as bronchiectasis, wall thickening, mucus plugs, bronchiolar impactions and consolidations. Metrics obtained with the 3D architecture were superior for mucus plugs, bronchiolar impactions and consolidations (p<0.001) but not significantly different for bronchiectasis and wall thickening (p>0.05). ConclusionAI with the 3D-nnUnet model can perform fully automated segmentation of CF-related structural hallmarks on CT scans, and overcome 2D implementation. Non-invasive, holistic 3D quantifications are allowed for promising next clinical applications.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Diagnostic value of chest ultrasound in children with cystic fibrosis. 94%
- Enhancing Semantic Segmentation in Chest X-Ray Images through Image Preprocessing: ps-KDE for Pixel-wise Substitution by Kernel Density Estimation 94%
- ai-corona : Radiologist-Assistant Deep Learning Framework for COVID-19 Diagnosis in Chest CT Scans 93%
Similar papers in this journal
- MultiCOVID: a multi modal Deep Learning approach for COVID-19 diagnosis 95%
- Toward Understanding COVID-19 Pneumonia: A Deep-learning-based Approach for Severity Analysis and Monitoring the Disease 94%
- High-Dimensional Multinomial Multiclass Severity Scoring of COVID-19 Pneumonia Using CT Radiomics Features and Machine Learning Algorithms 94%
Similar papers in this journal
- Passive Microwave Radiometry (MWR) for diagnostics of COVID-19 lung complications in Kyrgyzstan 93%
- Auto-detection of motion artifacts on CT pulmonary angiograms with a physician-trained AI algorithm 92%
- Detection, Isolation and Quantification of Myocardial Infarct with Four Different Histological Staining Techniques 90%
Similar papers in this journal
- Two-Step Machine Learning to Diagnose and Predict Involvement of Lungs in COVID-19 and Pneumonia using CT Radiomics 95%
- The mathematics of erythema: Development of machine learning models for artificial intelligence assisted measurement and severity scoring of radiation induced dermatitis 92%
- Deep learning ensemble for abdominal aortic calcification scoring from lumbar spine X-ray and DXA images 91%
Similar papers in this journal
- Automated airway quantification associates with mortality in idiopathic pulmonary fibrosis 96%
- From Community Acquired Pneumonia to COVID-19: A Deep Learning Based Method for Quantitative Analysis of COVID-19 on thick-section CT Scans 92%
- A deep learning algorithm using CT images to screen for Corona Virus Disease (COVID-19) 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.