medical-5

COVID-19 Image Segmentation

3260 papers • 126 benchmarks • 313 datasets

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AlexTS1980/COVID-CT-Mask-Net

3 papers 28

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Most implemented papers

Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT

K. Cao, Lin Li, Lixin Qin, Zeguo Xu, Youbing Yin, Xin Wang, B. Kong, Junjie Bai, Yi Lu, Zhenghan Fang, Q. Song, Daliang Liu, Guisheng Wang, Qizhong Xu, Xisheng Fang, Shiqin Zhang, J. Xia, Jun Xia•Wed Mar 18 2020

A deep learning model can accurately detect COVID-19 and differentiate it from community acquired pneumonia and other lung diseases.

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

D. Shen, Yaozong Gao, F. Shan, Jun Wang, Weiya Shi, N. Shi, Miaofei Han, Zhong Xue, Yuxin Shi•Mon Mar 09 2020

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 and 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.

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AI Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Etiology on Chest CT

Harrison X. Bai, Robin Wang, Zeng Xiong, Ben Hsieh, Ken Chang, K. Halsey, Thi My Linh Tran, Ji Whae Choi, Dongcui Wang, Linbo Shi, Ji Mei, Xiao-Long Jiang, I-Yin Pan, Qiuhua Zeng, Ping-Feng Hu, Yi-Hui Li, Fei-Xian Fu, Raymond Y. Huang, R. Sebro, Qi-Zhi Yu, Michael K Atalay, W. Liao•Sun Apr 26 2020

AI assistance improved radiologists' performance in distinguishing COVID-19 from non-COVID-19 pneumonia on chest CT and assess radiologist performance without and with AI assistance.

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CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image

M. Hosseinzadeh, B. Haibe-Kains, Tahereh Javaheri, Morteza Homayounfar, Zohreh Amoozgar, R. Reiazi, F. Homayounieh, Engy Abbas, A. Laali, A. Radmard, M. Gharib, Seyed Ali Javad Mousavi, Omid Ghaemi, Rosa Babaei, Hadi Karimi Mobin, Rana Jahanban-Esfahlan, K. Seidi, M. Kalra, Guanglan Zhang, L. Chitkushev, R. Malekzadeh, Reza Rawassizadeh•Tue May 05 2020

An open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases and increases the accuracy of CT imaging detection to 90% compared to radiologists (70%).

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RANDGAN: Randomized generative adversarial network for detection of COVID-19 in chest X-ray

Saman Motamed, P. Rogalla, F. Khalvati•Mon Oct 05 2020

A randomized generative adversarial network (RANDGAN) is proposed that detects images of an unknown class (COVID-19) from known and labelled classes (Normal and Viral Pneumonia) without the need for labels and training data from the unknown class of images (CO VID-19).

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COVID-CT-Mask-Net: prediction of COVID-19 from CT scans using regional features

Aram Ter-Sarkisov•Tue Oct 13 2020

The model works in two stages: in the first stage, Mask R-CNN is trained to localize and detect two types of lesions in images, and in the second stage, these detections are fused to classify the whole input image.

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Lightweight Model For The Prediction of COVID-19 Through The Detection And Segmentation of Lesions in Chest CT Scans

Aram Ter-Sarkisov•Tue Nov 03 2020

A lightweight Mask R-CNN model that segments areas with the Ground Glass Opacity and Consolidation in chest CT scans using truncated ResNet18 and ResNet34 nets with a single layer of Feature Pyramid Network as a backbone net is introduced.

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Detection and Segmentation of Lesion Areas in Chest CT Scans For The Prediction of COVID-19

Aram Ter-Sarkisov•Mon Oct 26 2020

This paper compares the models for the detection and segmentation of Ground Glass Opacity and Consolidation in chest CT scans and analyzes the effect of Non-Maximum Suppression of overlapping object predictions, both on the segmentation and classification accuracy.

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One Shot Model For COVID-19 Classification and Lesions Segmentation In Chest CT Scans Using LSTM With Attention Mechanism

Aram Ter-Sarkisov•Thu Feb 18 2021

A model that fuses instance segmentation, Long Short-Term Memory Network and Attention mechanism to predict COVID-19 and segment chest CT scans, and is trained in one shot.

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Longitudinal Quantitative Assessment of COVID-19 Infection Progression from Chest CTs

R. Braren, N. Navab, Matthias Keicher, Tobias Czempiel, Magdalini Paschali, S. T. Kim, Leili Goli, Ashkan Khakzar, E. Burian, T. Wendler•Thu Mar 11 2021

A new framework to identify infection at a voxel level (identification of healthy lung, consolidation, and ground-glass opacity) and visualize the progression of COVID-19 using sequential low-dose non-contrast CT scans is proposed and a longitudinal segmentation network is devised that utilizes the reference scan information to improve the performance of disease identification.

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