Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images

Gao, Kai; Su, Jianpo; Jiang, Zhongbiao; Zeng, Ling; Feng, Zhichao; Shen, Hui; Rong, Pengfei; Xu, Xin; Qin, Jian; Yang, Yuexiang; Wang, Wei; Hu, Dewen

doi:10.1016/j.media.2020.101836

Cited by 160 publications

(101 citation statements)

References 41 publications

(44 reference statements)

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“…In outbreak areas, COVID-19 patients are in urgent need of diagnosis. Due to fast acquisition, some works perform X-ray ( Wong, Lam, Fong, Leung, Chin, Lo, Lui, Lee, Chiu, Chung, Lee, Wan, Hung, Lam, Kuo, Ng, 2019 , Sitaula, Hossain, 2020 , Minaee, Kafieh, Sonka, Yazdani, Soufi, 2020 ) and CT scans ( Di, Shi, Yan, Xia, Mo, Ding, Shan, Li, Wei, Shao, Han, Gao, Sui, Gao, Shen, 2020 , Yang, Xu, Li, Myronenko, Roth, Harmon, Xu, Turkbey, Turkbey, Wang, Zhu, Carrafiello, Patella, Cariati, Obinata, Mori, Tamura, An, Wood, Xu, 2021 , Gao, Su, Jiang, Zeng, Feng, Shen, Rong, Xu, Qin, Yang, Wang, Hu, 2020 ) to identify COVID-19. Besides early screening, the study of malignant progression prediction is also important for treatment planning.…”

Section: Related Workmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2021

Medical Image Analysis

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Section: Related Workmentioning

confidence: 99%

Deep learning for predicting COVID-19 malignant progression

Fang

Bai

Chen

et al. 2021

Medical Image Analysis

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“…12 (2.7%) of the assessed papers were assigned high maturity 26,27,28,29,30,31,32,33,34,35,36,37 . The list of papers together with details about their task, key finding, implementation and results appear in Table 1 and are further discussed in this section.…”

Section: Resultsmentioning

confidence: 99%

“…While most authors of highly mature studies released their code (indeed three papers did not release code 28,31,37 ) only a third of them released at least part of their data. This raises concerns about reproducibility and transparency of their studies, as recently argued against a Nature study on breast cancer screening 11 in a “ matters arising” 58 .…”

Section: Discussionmentioning

confidence: 99%

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

Born

Beymer

Rajan

et al. 2020

Preprint

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During the COVID-19 pandemic, lung imaging takes a key role in addressing the magnified need of speed, cost, ubiquity and precision in medical care. The rise of artificial intelligence induced a quantum leap in medical imaging: AI has now proven equipollent to healthcare professionals in several diseases and the potential to save time, cost and increase coverage. But AI-accelerated medical imaging must still fully demonstrate its ability in remediating diseases such as COVID-19. We identify key use cases of lung imaging for COVID-19, comparing CT, X-Ray and ultrasound imaging from clinical and AI perspectives. We perform a systematic, manual survey of 197 related publications that reveals a disparity in the focus of the AI and clinical communities, caused by data availability and the lack of collaboration, and in modality trends, driven by ubiquity. Last, challenges in AI-acceleration and ways to remediate them are discussed and future research goals are identified.

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“…The clinical and laboratory data were obtained with data collection forms from electronic medical records. To accurately quantify the extent of lung involvement on the non-contrast chest CT images, we adopted a U-Net++ DL network developed by our team for the three-dimensional segmentation of lung and lesions (Supplementary Figure 2 ) [ 16 ]. Furthermore, we proposed an unsupervised multi-scale texture feature clustering method to distinguish between ground-glass opacification (GGO) and consolidation (CON) [ 17 ].…”

Section: Methodsmentioning

confidence: 99%

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

et al. 2021

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Objectives To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. Methods We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning–based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. Results There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936–0.976) and 0.953 (95% CI: 0.891–0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. Conclusions We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. Key Points • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning–based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19. Supplementary Information The online version contains supplementary material available at 10.1007/s00330-021-07957-z.

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Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images

Cited by 160 publications

References 41 publications

Deep learning for predicting COVID-19 malignant progression

Deep learning for predicting COVID-19 malignant progression

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19

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