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Bougourzi F, Distante C, Dornaika F, Taleb-Ahmed A, Hadid A, Chaudhary S, Yang W, Qiang Y, Anwar T, Breaban ME, Hsu CC, Tai SC, Chen SN, Tricarico D, Chaudhry HAH, Fiandrotti A, Grangetto M, Spatafora MAN, Ortis A, Battiato S. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge. Sensors (Basel) 2024; 24:1557. [PMID: 38475092 PMCID: PMC10934842 DOI: 10.3390/s24051557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/29/2023] [Accepted: 02/26/2024] [Indexed: 03/14/2024]
Abstract
COVID-19 analysis from medical imaging is an important task that has been intensively studied in the last years due to the spread of the COVID-19 pandemic. In fact, medical imaging has often been used as a complementary or main tool to recognize the infected persons. On the other hand, medical imaging has the ability to provide more details about COVID-19 infection, including its severity and spread, which makes it possible to evaluate the infection and follow-up the patient's state. CT scans are the most informative tool for COVID-19 infection, where the evaluation of COVID-19 infection is usually performed through infection segmentation. However, segmentation is a tedious task that requires much effort and time from expert radiologists. To deal with this limitation, an efficient framework for estimating COVID-19 infection as a regression task is proposed. The goal of the Per-COVID-19 challenge is to test the efficiency of modern deep learning methods on COVID-19 infection percentage estimation (CIPE) from CT scans. Participants had to develop an efficient deep learning approach that can learn from noisy data. In addition, participants had to cope with many challenges, including those related to COVID-19 infection complexity and crossdataset scenarios. This paper provides an overview of the COVID-19 infection percentage estimation challenge (Per-COVID-19) held at MIA-COVID-2022. Details of the competition data, challenges, and evaluation metrics are presented. The best performing approaches and their results are described and discussed.
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Affiliation(s)
- Fares Bougourzi
- Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy;
- Laboratoire LISSI, University Paris-Est Creteil, Vitry sur Seine, 94400 Paris, France
| | - Cosimo Distante
- Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy;
| | - Fadi Dornaika
- Department of Computer Science and Artificial Intelligence, University of the Basque Country UPV/EHU, Manuel Lardizabal, 1, 20018 San Sebastian, Spain;
- IKERBASQUE, Basque Foundation for Science, 48011 Bilbao, Spain
| | - Abdelmalik Taleb-Ahmed
- Institut d’Electronique de Microélectronique et de Nanotechnologie (IEMN), UMR 8520, Universite Polytechnique Hauts-de-France, Université de Lille, CNRS, 59313 Valenciennes, France;
| | - Abdenour Hadid
- Sorbonne Center for Artificial Intelligence, Sorbonne University of Abu Dhabi, Abu Dhabi P.O. Box 38044, United Arab Emirates
| | - Suman Chaudhary
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (S.C.)
| | - Wanting Yang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (S.C.)
| | - Yan Qiang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (S.C.)
| | - Talha Anwar
- School of Computing, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan
| | | | - Chih-Chung Hsu
- Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
| | - Shen-Chieh Tai
- Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
| | - Shao-Ning Chen
- Institute of Data Science, National Cheng Kung University, No. 1, University Rd., East Dist., Tainan City 701, Taiwan
| | - Davide Tricarico
- Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy; (D.T.); (H.A.H.C.)
| | - Hafiza Ayesha Hoor Chaudhry
- Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy; (D.T.); (H.A.H.C.)
| | - Attilio Fiandrotti
- Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy; (D.T.); (H.A.H.C.)
| | - Marco Grangetto
- Dipartimento di Informatica, Universita degli Studi di Torino, Corso Svizzera 185, 10149 Torino, Italy; (D.T.); (H.A.H.C.)
| | | | - Alessandro Ortis
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy (S.B.)
| | - Sebastiano Battiato
- Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy (S.B.)
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