AI-Driven CT-based quantification, staging and short-term outcome prediction of COVID-19 pneumonia

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

The segmentation tool was built under the paradigm of ensemble methods using a 2D fully convolutional network together with the AtlasNet framework 22 and a 3D fully convolutional network 25 . The AtlasNet framework combines a registration stage of the CT scans to a number of anatomical templates and consequently utilizes multiple deep learning-based classifiers trained for each template. At the end, the prediction of each model is -to the original anatomy and a majority voting scheme is used to produce the final projection, combining the results of the different networks. A major advantage of the AtlasNet framework is that it incorporates a natural data augmentation by registering each CT scan to several templates. Moreover, the framework is agnostic to the segmentation model that will be utilized. For the registration of the CT scans to the templates, an elastic registration framework based on Markov Random Fields was used, providing the optimal displacements for each template 33 .