ArticleIEEE transactions on medical imaging2020
A Rapid, Accurate and Machine-Agnostic Segmentation and Quantification Method for CT-Based COVID-19 Diagnosis.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 69 papers, 1 of them a synthesis that pooled it.
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
69 citing papers in PubMed, 1 synthesis or guideline pooled it, 197 citations in OpenAlex.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
- Unveiling the Benefits of Artificial Intelligence in Individual, Organizational Management, and the Health/Sector System.Health science reports · 2026Review
- Comparing Different Data Partitioning Strategies for Segmenting Areas Affected by COVID-19 in CT Scans.Diagnostics (Basel, Switzerland) · 2024Article
- Self-supervised learning framework application for medical image analysis: a review and summary.Biomedical engineering online · 2024Review
- An AI-Based Low-Risk Lung Health Image Visualization Framework Using LR-ULDCT.Journal of imaging informatics in medicine · 2024Article
- Semi-supervised CT image segmentation via contrastive learning based on entropy constraints.Biomedical engineering letters · 2024Article
- Incorporation of "Artificial Intelligence" for Objective Pain Assessment: A Comprehensive Review.Pain and therapy · 2024Review
- A unified method to revoke the private data of patients in intelligent healthcare with audit to forget.Nature communications · 2023Article
- Bibliometric analysis of the use of artificial intelligence in COVID-19 based on scientific studies.Health science reports · 2023Article
- Automatic COVID-19 and Common-Acquired Pneumonia Diagnosis Using Chest CT Scans.Bioengineering (Basel, Switzerland) · 2023Article
- MIC-Net: A deep network for cross-site segmentation of COVID-19 infection in the fog-assisted IoMT.Information sciences · 2023Article
- Cov-TransNet: Dual branch fusion network with transformer for COVID-19 infection segmentation.Biomedical signal processing and control · 2023Article
- Bilateral adaptive graph convolutional network on CT based Covid-19 diagnosis with uncertainty-aware consensus-assisted multiple instance learning.Medical image analysis · 2023Article
- Combined Cloud-Based Inference System for the Classification of COVID-19 in CT-Scan and X-Ray Images.New generation computing · 2023Article
- MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images.Journal of multidisciplinary healthcare · 2023Article
- ERGPNet: lesion segmentation network for COVID-19 chest X-ray images based on embedded residual convolution and global perception.Frontiers in physiology · 2023Article
- Application of Machine Learning and Deep Learning Techniques for COVID-19 Screening Using Radiological Imaging: A Comprehensive Review.SN computer science · 2023Article
- Efficient COVID-19 super pixel segmentation algorithm using MCFO-based SLIC.Journal of ambient intelligence and humanized computing · 2023Article
- Active deep learning from a noisy teacher for semi-supervised 3D image segmentation: Application to COVID-19 pneumonia infection in CT.Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society · 2022Article
- SARS-CoV-2 Morphometry Analysis and Prediction of Real Virus Levels Based on Full Recurrent Neural Network Using TEM Images.Viruses · 2022Article
9 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
16 authors at 8 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
COVID-19 has caused a global pandemic and become the most urgent threat to the entire world. Tremendous efforts and resources have been invested in developing diagnosis, prognosis and treatment strategies to combat the disease. Although nucleic acid detection has been mainly used as the gold standard to confirm this RNA virus-based disease, it has been shown that such a strategy has a high false negative rate, especially for patients in the early stage, and thus CT imaging has been applied as a major diagnostic modality in confirming positive COVID-19. Despite the various, urgent advances in developing artificial intelligence (AI)-based computer-aided systems for CT-based COVID-19 diagnosis, most of the existing methods can only perform classification, whereas the state-of-the-art segmentation method requires a high level of human intervention. In this paper, we propose a fully-automatic, rapid, accurate, and machine-agnostic method that can segment and quantify the infection regions on CT scans from different sources. Our method is founded upon two innovations: 1) the first CT scan simulator for COVID-19, by fitting the dynamic change of real patients' data measured at different time points, which greatly alleviates the data scarcity issue; and 2) a novel deep learning algorithm to solve the large-scene-small-object problem, which decomposes the 3D segmentation problem into three 2D ones, and thus reduces the model complexity by an order of magnitude and, at the same time, significantly improves the segmentation accuracy. Comprehensive experimental results over multi-country, multi-hospital, and multi-machine datasets demonstrate the superior performance of our method over the existing ones and suggest its important application value in combating the disease.
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.