Inter-Variability Study of COVLIAS 1.0: Hybrid Deep Learning Models for COVID-19 Lung Segmentation in Computed Tomography

<i>Background</i>: For COVID-19 lung severity, segmentation of lungs on computed tomography (CT) is the first crucial step. Current deep learning (DL)-based Artificial Intelligence (AI) models have a bias in the training stage of segmentation because only one set of ground truth (GT) annotations are...

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Egile Nagusiak:	Jasjit S. Suri, Sushant Agarwal, Pranav Elavarthi, Rajesh Pathak, Vedmanvitha Ketireddy, Marta Columbu, Luca Saba, Suneet K. Gupta, Gavino Faa, Inder M. Singh, Monika Turk, Paramjit S. Chadha, Amer M. Johri, Narendra N. Khanna, Klaudija Viskovic, Sophie Mavrogeni, John R. Laird, Gyan Pareek, Martin Miner, David W. Sobel, Antonella Balestrieri, Petros P. Sfikakis, George Tsoulfas, Athanasios Protogerou, Durga Prasanna Misra, Vikas Agarwal, George D. Kitas, Jagjit S. Teji, Mustafa Al-Maini, Surinder K. Dhanjil, Andrew Nicolaides, Aditya Sharma, Vijay Rathore, Mostafa Fatemi, Azra Alizad, Pudukode R. Krishnan, Ferenc Nagy, Zoltan Ruzsa, Archna Gupta, Subbaram Naidu, Mannudeep K. Kalra
Formatua:	Artigo
Hizkuntza:	Inglês
Argitaratua:	MDPI AG 2021-11-01
Saila:	Diagnostics
Gaiak:	COVID-19 computed tomography lungs variability segmentation hybrid deep learning
Sarrera elektronikoa:	https://www.mdpi.com/2075-4418/11/11/2025
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