COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge

Fares Bougourzi (1) , Cosimo Distante (1) , Fadi Dornaika (2, 3, 4) , Abdelmalik Taleb-Ahmed (5) , Abdenour Hadid (6) , Suman Chaudhary , Wanting Yang , Yan Qiang , Talha Anwar , Mihaela Elena Breaban (7) , Chih-Chung Hsu (8) , Shen-Chieh Tai (8) , Shao-Ning Chen (8) , Davide Tricarico (9) , Hafiza Ayesha Hoor Chaudhry (9) , Attilio Fiandrotti (9) , Marco Grangetto (9) , Maria Ausilia Napoli Spatafora (10) , Alessandro Ortis (10) , Sebastiano Battiato (10)

1 ISASI - Istituto di Scienze Applicate e Sistemi Intelligenti “Eduardo Caianiello”
2 UPV / EHU - University of the Basque Country = Euskal Herriko Unibertsitatea
3 Henan University
4 Ikerbasque - Basque Foundation for Science
5 COMNUM - IEMN - COMmunications NUMériques - IEMN
6 Sorbonne university Abu Dhabi
7 UAIC - Alexandru Ioan Cuza University of Iași = Universitatea Alexandru Ioan Cuza din Iași
8 NCKU - National Cheng Kung University
9 UNITO - Università degli studi di Torino = University of Turin
10 Unict - Università degli studi di Catania = University of Catania

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

Keywords: COVID-19; convolutional neural network; deep learning; segmentation; Per-COVID-19; transformer; estimation

Domains: Medical Imaging; Machine Learning [cs.LG]; Emerging diseases; Infectious diseases; Pulmonology and respiratory tract

Fares Bougourzi, Cosimo Distante, Fadi Dornaika, Abdelmalik Taleb-Ahmed, Abdenour Hadid, et al.. COVID-19 Infection Percentage Estimation from Computed Tomography Scans: Results and Insights from the International Per-COVID-19 Challenge. Sensors, 2024, 24, ⟨10.3390/s24051557⟩. ⟨hal-04524991⟩