ArticleJournal of imaging2020
Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies.
Article in Journal of imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Clinical applications of deep learning in distinguishing benign from malignant pulmonary nodules in computed tomography scans.Archives of medical science : AMS · 2026Article
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- AI-driven genetic algorithm-optimized lung segmentation for precision in early lung cancer diagnosis.Scientific reports · 2025Article
- Deep learning-based fully automated detection and segmentation of pelvic lymph nodes on diffusion-weighted images for prostate cancer: a multicenter study.Cancer imaging : the official publication of the International Cancer Imaging Society · 2025Article
- A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images.Diagnostics (Basel, Switzerland) · 2023Review
- Non-invasive prediction of overall survival time for glioblastoma multiforme patients based on multimodal MRI radiomics.International journal of imaging systems and technology · 2023Article
- DTLCx: An Improved ResNet Architecture to Classify Normal and Conventional Pneumonia Cases from COVID-19 Instances with Grad-CAM-Based Superimposed Visualization Utilizing Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- [Cancers · 2022Article
- Article
- A deep learning approach using effective preprocessing techniques to detect COVID-19 from chest CT-scan and X-ray images.Computers in biology and medicine · 2021Article
- Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.BMC medical imaging · 2021Article
- Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images.Journal of imaging · 2021Article
- State of the Art in Artificial Intelligence and Radiomics in Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2021Review
- Deep Learning-Based Methods for Prostate Segmentation in Magnetic Resonance Imaging.Applied sciences (Basel, Switzerland) · 2021Article
- In-Series U-Net Network to 3D Tumor Image Reconstruction for Liver Hepatocellular Carcinoma Recognition.Diagnostics (Basel, Switzerland) · 2020Article
- A Survey on Artificial Intelligence in Pulmonary Imaging.Wiley interdisciplinary reviews. Data mining and knowledge discoveryArticle
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Authors and funding
10 authors.
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Abstract
backgroundThe aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model.
methodsTwo deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources' requirements.
resultsE-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region.
conclusionsWe demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results.
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