Creating a training set for artificial intelligence from initial segmentations of airways

Dudurych, I.M.; Garcia-Uceda, Antonio; Saghir, Zaigham; Tiddens, Harm A.W.M.; Vliegenthart, Rozemarijn; Bruijne, Marleen de

doi:10.1186/s41747-021-00247-9

Cited by 3 publications

(4 citation statements)

References 34 publications

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“…These models demonstrated an impressive accuracy of 84.6% (AUC-0.89). In contrast, Lung-RADS achieved an accuracy of 72.2% (AUC-0.77) [39].…”

Section: Nodule Segmentation and Characterizationmentioning

confidence: 84%

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Gandhi,

Gurram,

Amgai

et al. 2023

Cancers

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Lung cancer remains one of the leading causes of cancer-related deaths worldwide, emphasizing the need for improved diagnostic and treatment approaches. In recent years, the emergence of artificial intelligence (AI) has sparked considerable interest in its potential role in lung cancer. This review aims to provide an overview of the current state of AI applications in lung cancer screening, diagnosis, and treatment. AI algorithms like machine learning, deep learning, and radiomics have shown remarkable capabilities in the detection and characterization of lung nodules, thereby aiding in accurate lung cancer screening and diagnosis. These systems can analyze various imaging modalities, such as low-dose CT scans, PET-CT imaging, and even chest radiographs, accurately identifying suspicious nodules and facilitating timely intervention. AI models have exhibited promise in utilizing biomarkers and tumor markers as supplementary screening tools, effectively enhancing the specificity and accuracy of early detection. These models can accurately distinguish between benign and malignant lung nodules, assisting radiologists in making more accurate and informed diagnostic decisions. Additionally, AI algorithms hold the potential to integrate multiple imaging modalities and clinical data, providing a more comprehensive diagnostic assessment. By utilizing high-quality data, including patient demographics, clinical history, and genetic profiles, AI models can predict treatment responses and guide the selection of optimal therapies. Notably, these models have shown considerable success in predicting the likelihood of response and recurrence following targeted therapies and optimizing radiation therapy for lung cancer patients. Implementing these AI tools in clinical practice can aid in the early diagnosis and timely management of lung cancer and potentially improve outcomes, including the mortality and morbidity of the patients.

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“…These models demonstrated an impressive accuracy of 84.6% (AUC-0.89). In contrast, Lung-RADS achieved an accuracy of 72.2% (AUC-0.77) [39].…”

Section: Nodule Segmentation and Characterizationmentioning

confidence: 84%

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Gandhi,

Gurram,

Amgai

et al. 2023

Cancers

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“…For lumen segmentation, good results could be readily achieved by using the publicly available trained model bundled with Bronchinet [ 3 ], which uses airway segmentations for training from the Danish Lung Cancer Screening Trial [ 32 ] in combination with an Erasmus-MC Sophia (cystic fibrosis) dataset [ 33 ]. The ImaLife scan protocol has a lower radiation dose with a total DLP of < 100 mGycm, and more noise in the scans; retraining the tools resulted in better performance [ 13 ]. For maximum performance on different datasets, optimising the pipeline for the target CT protocol may be necessary.…”

Section: Discussionmentioning

confidence: 99%

“…We used a deep learning airway segmentation method (Bronchinet) [ 3 ], based on a 3D U-Net model, to automatically obtain airway lumen segmentation from the CT scans. For training, we used a dataset of 24 ImaLife scans to train Bronchinet from scratch, with ground truth airway segmentations generated with a previously reported method [ 13 ]. From the full dataset, we used 22 scans for training (i.e., optimising the model weights) and the remaining 2 scans for validation (i.e., early stopping and model convergence).…”

Section: Methodsmentioning

confidence: 99%

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

et al. 2023

Self Cite

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Objectives Computed tomography (CT)–based bronchial parameters correlate with disease status. Segmentation and measurement of the bronchial lumen and walls usually require significant manpower. We evaluate the reproducibility of a deep learning and optimal-surface graph-cut method to automatically segment the airway lumen and wall, and calculate bronchial parameters. Methods A deep-learning airway segmentation model was newly trained on 24 Imaging in Lifelines (ImaLife) low-dose chest CT scans. This model was combined with an optimal-surface graph-cut for airway wall segmentation. These tools were used to calculate bronchial parameters in CT scans of 188 ImaLife participants with two scans an average of 3 months apart. Bronchial parameters were compared for reproducibility assessment, assuming no change between scans. Results Of 376 CT scans, 374 (99%) were successfully measured. Segmented airway trees contained a mean of 10 generations and 250 branches. The coefficient of determination (R2) for the luminal area (LA) ranged from 0.93 at the trachea to 0.68 at the 6th generation, decreasing to 0.51 at the 8th generation. Corresponding values for Wall Area Percentage (WAP) were 0.86, 0.67, and 0.42, respectively. Bland–Altman analysis of LA and WAP per generation demonstrated mean differences close to 0; limits of agreement (LoA) were narrow for WAP and Pi10 (± 3.7% of mean) and wider for LA (± 16.4–22.8% for 2–6th generations). From the 7th generation onwards, there was a sharp decrease in reproducibility and a widening LoA. Conclusion The outlined approach for automatic bronchial parameter measurement on low-dose chest CT scans is a reliable way to assess the airway tree down to the 6th generation. Statement on clinical relevance This reliable and fully automatic pipeline for bronchial parameter measurement on low-dose CT scans has potential applications in screening for early disease and clinical tasks such as virtual bronchoscopy or surgical planning, while also enabling the exploration of bronchial parameters in large datasets. Key Points • Deep learning combined with optimal-surface graph-cut provides accurate airway lumen and wall segmentations on low-dose CT scans. • Analysis of repeat scans showed that the automated tools had moderate-to-good reproducibility of bronchial measurements down to the 6thgeneration airway. • Automated measurement of bronchial parameters enables the assessment of large datasets with less man-hours.

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“…Moreover, it supports extensions, with over a hundred open-source extensions available on the platform. These extensions range from radiomics analysis [10,11] to artificial intelligence (AI)-based automatic organ segmentation for medical image analysis [12,13] and from surgical navigation [14,15] to target delineation [16,17] and dose calculation for radiation therapy clinical tools [18,19]. Its extensive functionality surpasses that of professional workstations utilized in clinical environments [20][21][22][23][24].…”

Section: Application Of 3d Slicer Platform In Medical Image Analysismentioning

confidence: 99%

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

Zhang,

Feng,

Zhao

et al. 2024

Diagnostics

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Artificial Intelligence (AI) has revolutionized medical imaging procedures, specifically with regard to image segmentation, reconstruction, interpretation, and research. 3D Slicer, an open-source medical image analysis platform, has become a valuable tool in medical imaging education due to its integration of various AI applications. Through its open-source architecture, students can gain practical experience with diverse medical images and the latest AI technology, reinforcing their understanding of anatomy and imaging technology while fostering independent learning and clinical reasoning skills. The implementation of this platform improves instruction quality and nurtures skilled professionals who can meet the demands of clinical practice, research institutions, and technology innovation enterprises. AI algorithms’ application in medical image processing have facilitated their translation from the lab to practical clinical applications and education.

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Creating a training set for artificial intelligence from initial segmentations of airways

Cited by 3 publications

References 34 publications

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Artificial Intelligence and Lung Cancer: Impact on Improving Patient Outcomes

Reproducibility of a combined artificial intelligence and optimal-surface graph-cut method to automate bronchial parameter extraction

Exploring the Application of the Artificial-Intelligence-Integrated Platform 3D Slicer in Medical Imaging Education

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