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

For the 2D experiments we used classic stochastic gradient descent for the optimization with initial learning rate = 0.01, decrease of learning rate = 2.5 · 10 −3 every 10 epochs, momentum =0.9 and weight decay =5 · 10 −4 . For the 3D experiments we used the AMSGrad and a learning rate of 0.001. The training of a single network for both 2D and 3D network was completed in approximately 12 hours using a GeForce GTX 1080 GPU, while the prediction for a single CT scan was done in a few seconds. Training and validation curves for one template of AtlasNet and the 3D network are shown in Figure 6 . Both Dice similarity score and Haussdorff distances were higher with the 2D approach compared to the 3D approach (Figure ??) . However, the combination of their probability scores led to a significant improvement. Thus, the ensemble of 2D and 3D architectures was selected for the final COVID-19 segmentation tool.