Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Roth, Holger R.; Xu, Ziyue; Díez, Carlos Tor; Jacob, Ramon Sanchez; Zember, Jonathan; Moltó, José; Li, Wenqi; Xu, Sheng; Türkbey, Barış; Türkbey, Evrim; Yang, Dong; Harouni, Ahmed; Rieke, Nicola; Hu, Shishuai; Isensee, Fabian; Tang, Claire; Yu, Qinji; Sölter, Jan; Zheng, Tong; Liauchuk, Vitali; Zhou, Ziqi; Moltz, Jan Hendrik; Oliveira, Bruno; Xia, Yong; Maier‐Hein, Klaus H.; Li, Qikai; Husch, Andreas; Kovalev, Vassili; Li, Kang; Hering, Alessa; Vilaça, João L.; Flores, Mona G.; Xu, Daguang; Wood, Bradford J.; Linguraru, Marius George

doi:10.1016/j.media.2022.102605

Cited by 36 publications

(23 citation statements)

References 57 publications

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“…This study included two types of datasets: public and private. The public dataset consisted of two different data sources: COVID-19 lung CT lesion segmentation challenge (An et al, 2020;Roth et al, 2022) (https://covid-segmentation.grandchallenge.org/Data/) and MosMeddata (Morozov et al, 2020) (https://www.kaggle.com/datasets/andrewmvd/mosmed-covid19ct-scans). The first public dataset contained images of 249 COVID-19 patients, while the second contained images of 856 COVID-19 patients and 254 non-pneumonia patients (n = 1,110).…”

Section: Patientsmentioning

confidence: 99%

Computed tomography–based COVID–19 triage through a deep neural network using mask–weighted global average pooling

Zhang

Sun²,

Zhou³

et al. 2023

Front. Cell. Infect. Microbiol.

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BackgroundThere is an urgent need to find an effective and accurate method for triaging coronavirus disease 2019 (COVID-19) patients from millions or billions of people. Therefore, this study aimed to develop a novel deep-learning approach for COVID-19 triage based on chest computed tomography (CT) images, including normal, pneumonia, and COVID-19 cases.MethodsA total of 2,809 chest CT scans (1,105 COVID-19, 854 normal, and 850 non-3COVID-19 pneumonia cases) were acquired for this study and classified into the training set (n = 2,329) and test set (n = 480). A U-net-based convolutional neural network was used for lung segmentation, and a mask-weighted global average pooling (GAP) method was proposed for the deep neural network to improve the performance of COVID-19 classification between COVID-19 and normal or common pneumonia cases.ResultsThe results for lung segmentation reached a dice value of 96.5% on 30 independent CT scans. The performance of the mask-weighted GAP method achieved the COVID-19 triage with a sensitivity of 96.5% and specificity of 87.8% using the testing dataset. The mask-weighted GAP method demonstrated 0.9% and 2% improvements in sensitivity and specificity, respectively, compared with the normal GAP. In addition, fusion images between the CT images and the highlighted area from the deep learning model using the Grad-CAM method, indicating the lesion region detected using the deep learning method, were drawn and could also be confirmed by radiologists.ConclusionsThis study proposed a mask-weighted GAP-based deep learning method and obtained promising results for COVID-19 triage based on chest CT images. Furthermore, it can be considered a convenient tool to assist doctors in diagnosing COVID-19.

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Section: Patientsmentioning

confidence: 99%

Computed tomography–based COVID–19 triage through a deep neural network using mask–weighted global average pooling

Zhang

Sun²,

Zhou³

et al. 2023

Front. Cell. Infect. Microbiol.

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“…Over the past decade, Deep Learning has enabled us to master many tasks that seemed impossible for machines ( Bougourzi et al, 2020 , Bougourzi et al, 2022 ). In particular, medical imaging tasks that are very complicated and require specialists and radiologists ( Prevedello et al, 2019 , Roth et al, 2022 ). Machine learning can provide an efficient solution to medical imaging tasks to assist and guide radiologists and physicians to reduce subjectivity and bias in decision-making.…”

Section: Related Workmentioning

confidence: 99%

PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans

Bougourzi

Distante

Dornaika

et al. 2023

Medical Image Analysis

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“…Pre-training Dataset: We collected a total of 1897 CT images to construct our pre-training dataset. They come from 4 public CT image datasets, including the ATM22 [27] (150 cases of chest), luna16 [28] (888 cases of lung), covid-19 [29] (448 cases of lung) and FLARE21 [30] (411 cases of abdomen) datasets.…”

Section: A Datasetsmentioning

confidence: 99%

HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation

Xing¹,

Li²,

Yu³

et al. 2023

Preprint

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Masked image modeling (MIM) with transformer backbones has recently been exploited as a powerful self-supervised pre-training technique. The existing MIM methods adopt the strategy to mask random patches of the image and reconstruct the missing pixels, which only considers semantic information at a lower level, and causes a long pre-training time. This paper presents Hy-bridMIM, a novel hybrid self-supervised learning method based on masked image modeling for 3D medical image segmentation. Specifically, we design a two-level masking hierarchy to specify which and how patches in sub-volumes are masked, effectively providing the constraints of higher level semantic information. Then we learn the semantic information of medical images at three levels, including: 1) partial region prediction to reconstruct key contents of the 3D image, which largely reduces the pre-training time burden (pixel-level); 2) patch-masking perception to learn the spatial relationship between the patches in each subvolume (region-level). and 3) drop-out-based contrastive learning between samples within a mini-batch, which further improves the generalization ability of the framework (sample-level). The proposed framework is versatile to support both CNN and transformer as encoder backbones, and also enables to pre-train decoders for image segmentation. We conduct comprehensive experiments on four widelyused public medical image segmentation datasets, including BraTS2020, BTCV, MSD Liver, and MSD Spleen. The experimental results show the clear superiority of Hybrid-MIM against competing supervised methods, masked pretraining approaches, and other self-supervised methods, in terms of quantitative metrics, timing performance and qualitative observations. The codes of HybridMIM are available at https://github.com/ge-xing/HybridMIM.

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Rapid artificial intelligence solutions in a pandemic—The COVID-19-20 Lung CT Lesion Segmentation Challenge

Cited by 36 publications

References 57 publications

Computed tomography–based COVID–19 triage through a deep neural network using mask–weighted global average pooling

Computed tomography–based COVID–19 triage through a deep neural network using mask–weighted global average pooling

PDAtt-Unet: Pyramid Dual-Decoder Attention Unet for Covid-19 infection segmentation from CT-scans

HybridMIM: A Hybrid Masked Image Modeling Framework for 3D Medical Image Segmentation

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