Selected article for: "care support and intensive care support"
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_4
    Snippet: In the context of this work, we report a deep learning-based segmentation tool to quantify COVID-19 disease and lung volume. For this purpose, we used an ensemble network approach inspired by the AtlasNet framework 22 . We investigated a combination of 2D slice-based 24 and 3D patch-based ensemble architectures 25 . The development of the deep learning-based segmentation solution was done on the basis of a multi-centric cohort of 478 unenhanced c.....
    Document: In the context of this work, we report a deep learning-based segmentation tool to quantify COVID-19 disease and lung volume. For this purpose, we used an ensemble network approach inspired by the AtlasNet framework 22 . We investigated a combination of 2D slice-based 24 and 3D patch-based ensemble architectures 25 . The development of the deep learning-based segmentation solution was done on the basis of a multi-centric cohort of 478 unenhanced chest CT scans (208,668 slices) of COVID-19 Figure 3 . Spider-chart distribution of features depicting their minimum and maximum values [mean value (blue), 70% percentile (yellow) and 90% percentile (red) lines] with respect to the different outcomes with the following order: top: non-severe, bottom left: intensive care support & bottom right: deceased in the testing set. White and red circles represent respectively 40% and 60% of the maximum value of each feature. Clear separation was observed on these feature space with respect to the non-severe & severe cases. In terms of deceased versus intensive care patients, notable difference were observed with respect to three variables, the age of the patient, the condition of the healthy lung and the non-uniformity of the disease (indicated with gray in the spider-chart).

    Search related documents:
    Co phrase search for related documents
    • care patient and CT scan: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25
    • care patient and different outcome: 1, 2, 3
    • care patient and ensemble architecture: 1
    • care patient and follow order: 1, 2, 3, 4
    • care support and CT scan: 1, 2, 3, 4, 5, 6, 7, 8, 9
    • care support and different outcome: 1, 2, 3, 4
    • CT scan and different outcome: 1, 2
    • CT scan and ensemble architecture: 1
    • CT scan and feature space: 1
    • CT scan and follow order: 1