Author: Guillaume Chassagnon; Maria Vakalopoulou; Enzo Battistella; Stergios Christodoulidis; Trieu-Nghi Hoang-Thi; Severine Dangeard; Eric Deutsch; Fabrice Andre; Enora Guillo; Nara Halm; Stefany El Hajj; Florian Bompard; Sophie Neveu; Chahinez Hani; Ines Saab; Alienor Campredon; Hasmik Koulakian; Souhail Bennani; Gael Freche; Aurelien Lombard; Laure Fournier; Hippolyte Monnier; Teodor Grand; Jules Gregory; Antoine Khalil; Elyas Mahdjoub; Pierre-Yves Brillet; Stephane Tran Ba; Valerie Bousson; Marie-Pierre Revel; Nikos Paragios
Title: AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia Document date: 2020_4_22
ID: nxm1jr0x_1
Snippet: . Comparison between automated and manual segmentations. Delineation of the diseased areas on chest CT in a COVID-19 patient: First Row: input, AI-segmentation, expert I-segmentation, expert II-segmentation. Second Row: Box-Plot Comparisons in terms of Dice similarity and Haussdorf between AI-solution, expert I & expert II, & Plot of correlation between disease extent automatically measured and the average disease extent measured from the 2 manua.....
Document: . Comparison between automated and manual segmentations. Delineation of the diseased areas on chest CT in a COVID-19 patient: First Row: input, AI-segmentation, expert I-segmentation, expert II-segmentation. Second Row: Box-Plot Comparisons in terms of Dice similarity and Haussdorf between AI-solution, expert I & expert II, & Plot of correlation between disease extent automatically measured and the average disease extent measured from the 2 manual segmentations. Disease extent is expressed as the percentage of lung affected by the disease. Third row: statistical measures on comparisons between AI, expert I, and expert II segmentations.
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