ArticleJournal of imaging2021
Customized Efficient Neural Network for COVID-19 Infected Region Identification in CT Images.
Article in Journal of imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Radiomics and Artificial Intelligence in Pulmonary Fibrosis.Journal of imaging informatics in medicine · 2025Review
- An attention-based deep learning for acute lymphoblastic leukemia classification.Scientific reports · 2024Article
- PSMA-positive prostatic volume prediction with deep learning based on T2-weighted MRI.La Radiologia medica · 2024Article
- Differentiation of retroperitoneal paragangliomas and schwannomas based on computed tomography radiomics.Scientific reports · 2023Article
- CT Rendering and Radiomic Analysis in Post-Chemotherapy Retroperitoneal Lymph Node Dissection for Testicular Cancer to Anticipate Difficulties for Young Surgeons.Journal of imaging · 2023Article
- EffViT-COVID: A dual-path network for COVID-19 percentage estimation.Expert systems with applications · 2023Article
- COVID-19 lung infection segmentation from chest CT images based on CAPA-ResUNet.International journal of imaging systems and technology · 2023Article
- A CT based radiomics nomogram for differentiation between focal-type autoimmune pancreatitis and pancreatic ductal adenocarcinoma.Frontiers in oncology · 2023Article
- Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine.Diagnostics (Basel, Switzerland) · 2022Review
- matRadiomics: A Novel and Complete Radiomics Framework, from Image Visualization to Predictive Model.Journal of imaging · 2022Article
- COVID-DAI: A novel framework for COVID-19 detection and infection growth estimation using computed tomography images.Microscopy research and technique · 2022Article
- COVIR: A virtual rendering of a novel NN architecture O-Net for COVID-19 Ct-scan automatic lung lesions segmentation.Computers & graphics · 2022Article
- A New Preclinical Decision Support System Based on PET Radiomics: A Preliminary Study on the Evaluation of an InnovativeJournal of imaging · 2022Article
- Deep Learning for Automated Contouring of Gross Tumor Volumes in Esophageal Cancer.Frontiers in oncology · 2022Article
- Development and validation of the 3D U-Net algorithm for segmentation of pelvic lymph nodes on diffusion-weighted images.BMC medical imaging · 2021Article
- Per-COVID-19: A Benchmark Dataset for COVID-19 Percentage Estimation from CT-Scans.Journal of imaging · 2021Article
- Estimation with Uncertainty via Conditional Generative Adversarial Networks.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundIn the field of biomedical imaging, radiomics is a promising approach that aims to provide quantitative features from images. It is highly dependent on accurate identification and delineation of the volume of interest to avoid mistakes in the implementation of the texture-based prediction model. In this context, we present a customized deep learning approach aimed at addressing the real-time, and fully automated identification and segmentation of COVID-19 infected regions in computed tomography images.
methodsIn a previous study, we adopted ENET, originally used for image segmentation tasks in self-driving cars, for whole parenchyma segmentation in patients with idiopathic pulmonary fibrosis which has several similarities to COVID-19 disease. To automatically identify and segment COVID-19 infected areas, a customized ENET, namely C-ENET, was implemented and its performance compared to the original ENET and some state-of-the-art deep learning architectures.
resultsThe experimental results demonstrate the effectiveness of our approach. Considering the performance obtained in terms of similarity of the result of the segmentation to the gold standard (dice similarity coefficient ~75%), our proposed methodology can be used for the identification and delineation of COVID-19 infected areas without any supervision of a radiologist, in order to obtain a volume of interest independent from the user.
conclusionsWe demonstrated that the proposed customized deep learning model can be applied to rapidly identify, and segment COVID-19 infected regions to subsequently extract useful information for assessing disease severity through radiomics analyses.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.