# Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

arXiv: https://arxiv.org/abs/2003.04655 · HTML: https://mlsanity.com/paper/2003.04655/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Mar 2020.

## Built on by (most cited first, 28 of 28)

- [Spider: A Unified Framework for Context-dependent Concept Segmentation](https://mlsanity.com/paper/2405.01002/) (2024)
- [BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2207.08114/) (2022)
- [M2SNet: Multi-scale in Multi-scale Subtraction Network for Medical Image Segmentation](https://mlsanity.com/paper/2303.10894/) (2023)
- [COVID-19 Detection and Analysis From Lung CT Images using Novel Channel Boosted CNNs](https://mlsanity.com/paper/2209.10963/) (2022)
- [Boundary Guided Semantic Learning for Real-time COVID-19 Lung Infection Segmentation System](https://mlsanity.com/paper/2209.02934/) (2022)
- [CT Image Synthesis Using Weakly Supervised Segmentation and Geometric Inter-Label Relations For COVID Image Analysis](https://mlsanity.com/paper/2106.10230/) (2021)
- [On Calibrated Model Uncertainty in Deep Learning](https://mlsanity.com/paper/2206.07795/) (2022)
- [Boosting COVID-19 Severity Detection with Infection-aware Contrastive Mixup Classification](https://mlsanity.com/paper/2211.14559/) (2022)
- [Personalized and privacy-preserving federated heterogeneous medical image analysis with PPPML-HMI](https://mlsanity.com/paper/2302.11571/) (2023)
- [Robust Weakly Supervised Learning for COVID-19 Recognition Using Multi-Center CT Images](https://mlsanity.com/paper/2112.04984/) (2021)
- [Understanding the Tricks of Deep Learning in Medical Image Segmentation: Challenges and Future Directions](https://mlsanity.com/paper/2209.10307/) (2022)
- [COVID-19 Infection Analysis Framework using Novel Boosted CNNs and Radiological Images](https://mlsanity.com/paper/2302.02619/) (2023)
- [Automated lung segmentation from CT images of normal and COVID-19 pneumonia patients](https://mlsanity.com/paper/2104.02042/) (2021)
- [Toward Generating Synthetic CT Volumes using a 3D-Conditional Generative Adversarial Network](https://mlsanity.com/paper/2104.02060/) (2021)
- [Semi-supervised Learning for COVID-19 Image Classification via ResNet](https://mlsanity.com/paper/2103.06140/) (2021)
- [Systematic investigation into generalization of COVID-19 CT deep learning models with Gabor ensemble for lung involvement scoring](https://mlsanity.com/paper/2105.15094/) (2021)
- [Adaptation to CT Reconstruction Kernels by Enforcing Cross-domain Feature Maps Consistency](https://mlsanity.com/paper/2203.14616/) (2022)
- [CoviLearn: A Machine Learning Integrated Smart X-Ray Device in Healthcare Cyber-Physical System for Automatic Initial Screening of COVID-19](https://mlsanity.com/paper/2106.05861/) (2021)
- [Learning from Pseudo Lesion: A Self-supervised Framework for COVID-19 Diagnosis](https://mlsanity.com/paper/2106.12313/) (2021)
- [AI-Powered Semantic Segmentation and Fluid Volume Calculation of Lung CT images in Covid-19 Patients](https://mlsanity.com/paper/2110.15558/) (2021)
- [Automated assessment of disease severity of COVID-19 using artificial intelligence with synthetic chest CT](https://mlsanity.com/paper/2112.05900/) (2021)
- [COVID-19 Detection Using CT Image Based On YOLOv5 Network](https://mlsanity.com/paper/2201.09972/) (2022)
- [Calibrated Bagging Deep Learning for Image Semantic Segmentation: A Case Study on COVID-19 Chest X-ray Image](https://mlsanity.com/paper/2206.00002/) (2022)
- [A review of Deep learning Techniques for COVID-19 identification on Chest CT images](https://mlsanity.com/paper/2208.00032/) (2022)
- [Comparative Analysis of State-of-the-Art Deep Learning Models for Detecting COVID-19 Lung Infection from Chest X-Ray Images](https://mlsanity.com/paper/2208.01637/) (2022)
- [MyI-Net: Fully Automatic Detection and Quantification of Myocardial Infarction from Cardiovascular MRI Images](https://mlsanity.com/paper/2212.13715/) (2022)
- [MEDNC: Multi-ensemble deep neural network for COVID-19 diagnosis](https://mlsanity.com/paper/2304.13135/) (2023)
- [Empowering Medical Imaging with Artificial Intelligence: A Review of Machine Learning Approaches for the Detection, and Segmentation of COVID-19 Using Radiographic and Tomographic Images](https://mlsanity.com/paper/2401.07020/) (2024)

## Abstract (arXiv)

CT imaging is crucial for diagnosis, assessment and staging COVID-19 infection. Follow-up scans every 3-5 days are often recommended for disease progression. It has been reported that bilateral and peripheral ground glass opacification (GGO) with or without consolidation are predominant CT findings in COVID-19 patients. However, due to lack of computerized quantification tools, only qualitative impression and rough description of infected areas are currently used in radiological reports. In this paper, a deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios w.r.t. the lung. The performance of the system was evaluated by comparing the automatically segmented infection regions with the manually-delineated ones on 300 chest CT scans of 300 COVID-19 patients. For fast manual delineation of training samples and possible manual intervention of automatic results, a human-in-the-loop (HITL) strategy has been adopted to assist radiologists for infection region segmentation, which dramatically reduced the total segmentation time to 4 minutes after 3 iterations of model updating. The average Dice simiarility coefficient showed 91.6% agreement between automatic and manual infaction segmentations, and the mean estimation error of percentage of infection (POI) was 0.3% for the whole lung. Finally, possible applications, including but not limited to analysis of follow-up CT scans and infection distributions in the lobes and segments correlated with clinical findings, were discussed.