ArticleInformatics in medicine unlocked2021
Robust chest CT image segmentation of COVID-19 lung infection based on limited data.
Article in Informatics in medicine unlocked, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 1 of them a synthesis that pooled it.
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Who cites it
35 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Medical image processing and COVID-19: A literature review and bibliometric analysis.Journal of infection and public health · 2022Pooled it
- Article
- A review on computer-aided diagnostic system to classify the disorders of the gastrointestinal tract.European journal of medical research · 2025Review
- Systematic Evaluation of Atrous Spatial Pyramid Pooling in U-Net for Pore Segmentation in Plasma Electrolytic Oxidation Coatings.Langmuir : the ACS journal of surfaces and colloids · 2025Article
- NCT-CXR: Enhancing Pulmonary Abnormality Segmentation on Chest X-Rays Using Improved Coordinate Geometric Transformations.Journal of imaging · 2025Article
- Identifying COVID-19-Infected Segments in Lung CT Scan Through Two Innovative Artificial Intelligence-Based Transformer Models.Archives of academic emergency medicine · 2025Article
- Human-in-the-Loop-A Deep Learning Strategy in Combination with a Patient-Specific Gaussian Mixture Model Leads to the Fast Characterization of Volumetric Ground-Glass Opacity and Consolidation in the Computed Tomography Scans of COVID-19 Patients.Journal of clinical medicine · 2024Article
- Development and validation of a deep learning-based framework for automated lung CT segmentation and acute respiratory distress syndrome prediction: a multicenter cohort study.EClinicalMedicine · 2024Article
- Hybrid transformer-CNN and LSTM model for lung disease segmentation and classification.PeerJ. Computer science · 2024Article
- A Systematic Review on Deep Structured Learning for COVID-19 Screening Using Chest CT from 2020 to 2022.Healthcare (Basel, Switzerland) · 2023Review
- ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images.Biomedical signal processing and control · 2023Article
- Remora Namib Beetle Optimization Enabled Deep Learning for Severity of COVID-19 Lung Infection Identification and Classification Using CT Images.Sensors (Basel, Switzerland) · 2023Article
- CRV-NET: Robust Intensity Recognition of Coronavirus in Lung Computerized Tomography Scan Images.Diagnostics (Basel, Switzerland) · 2023Article
- SAA-UNet: Spatial Attention and Attention Gate UNet for COVID-19 Pneumonia Segmentation from Computed Tomography.Diagnostics (Basel, Switzerland) · 2023Article
- COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net.Procedia computer science · 2023Article
- Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image.Biomedical signal processing and control · 2022Article
- A fast lightweight network for the discrimination of COVID-19 and pulmonary diseases.Biomedical signal processing and control · 2022Article
- A modified DeepLabV3+ based semantic segmentation of chest computed tomography images for COVID-19 lung infections.International journal of imaging systems and technology · 2022Article
- Automated Lung Segmentation from Computed Tomography Images of Normal and COVID-19 Pneumonia Patients.Iranian journal of medical sciences · 2022Article
- Effective multiscale deep learning model for COVID19 segmentation tasks: A further step towards helping radiologist.Neurocomputing · 2022Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe coronavirus disease 2019 (COVID-19) affects billions of lives around the world and has a significant impact on public healthcare. For quantitative assessment and disease monitoring medical imaging like computed tomography offers great potential as alternative to RT-PCR methods. For this reason, automated image segmentation is highly desired as clinical decision support. However, publicly available COVID-19 imaging data is limited which leads to overfitting of traditional approaches.
methodsTo address this problem, we propose an innovative automated segmentation pipeline for COVID-19 infected regions, which is able to handle small datasets by utilization as variant databases. Our method focuses on on-the-fly generation of unique and random image patches for training by performing several preprocessing methods and exploiting extensive data augmentation. For further reduction of the overfitting risk, we implemented a standard 3D U-Net architecture instead of new or computational complex neural network architectures.
resultsThrough a k-fold cross-validation on 20 CT scans as training and validation of COVID-19, we were able to develop a highly accurate as well as robust segmentation model for lungs and COVID-19 infected regions without overfitting on limited data. We performed an in-detail analysis and discussion on the robustness of our pipeline through a sensitivity analysis based on the cross-validation and impact on model generalizability of applied preprocessing techniques. Our method achieved Dice similarity coefficients for COVID-19 infection between predicted and annotated segmentation from radiologists of 0.804 on validation and 0.661 on a separate testing set consisting of 100 patients.
conclusionsWe demonstrated that the proposed method outperforms related approaches, advances the state-of-the-art for COVID-19 segmentation and improves robust medical image analysis based on limited data.
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