ArticleJournal of medical imaging (Bellingham, Wash.)2018
Deep convolutional neural networks for the automated segmentation of malignant pleural mesothelioma on computed tomography scans.
Article in Journal of medical imaging (Bellingham, Wash.), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Review
- Convolutional Neural Networks for Segmentation of Pleural Mesothelioma: Analysis of Probability Map Thresholds (CALGB 30901, Alliance).Journal of imaging informatics in medicine · 2025Article
- Transfer learning approach in pre-treatment CT images to predict therapeutic response in advanced malignant pleural mesothelioma.Frontiers in oncology · 2024Article
- Article
- Deep learning for diagnosis of malign pleural effusion on computed tomography images.Clinics (Sao Paulo, Brazil) · 2023Article
- Article
- ERS International Congress 2021: highlights from the Thoracic Surgery and Lung Transplantation Assembly.ERJ open research · 2022Article
- New Updates of the Imaging Role in Diagnosis, Staging, and Response Treatment of Malignant Pleural Mesothelioma.Cancers · 2021Review
- A review of deep learning based methods for medical image multi-organ segmentation.Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB) · 2021Review
- PleThora: Pleural effusion and thoracic cavity segmentations in diseased lungs for benchmarking chest CT processing pipelines.Medical physics · 2020Article
- Deep learning-based segmentation of malignant pleural mesothelioma tumor on computed tomography scans: application to scans demonstrating pleural effusion.Journal of medical imaging (Bellingham, Wash.) · 2020Article
- Predicting Mesothelioma Using Artificial Intelligence: A Scoping Review of Common Models and Applications.Technology in cancer research & treatmentArticle
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Tumor volume has been a topic of interest in the staging, prognostic evaluation, and treatment response assessment of malignant pleural mesothelioma (MPM). Deep convolutional neural networks (CNNs) were trained separately for the left and right hemithoraces on the task of differentiating between pleural thickening and normal thoracic tissue on computed tomography (CT) scans. A total of 4259 and 6192 axial sections containing segmented tumor were used to train the left-hemithorax CNN and the right-hemithorax CNN, respectively. Two distinct test sets of 131 sections from the CT scans of 43 patients were used to evaluate segmentation performance by calculating the Dice similarity coefficient (DSC) between deep CNN-generated tumor segmentations and reference tumor segmentations provided by a total of eight observers. Median DSC values ranged from 0.662 to 0.800 over the two test sets when comparing deep CNN-generated segmentations with observer reference segmentations. The deep CNN-based method achieved significantly higher DSC values for all three observers on the test set that allowed direct comparisons with a previously published automated segmentation method of MPM tumor on CT scans (
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