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 architecture of the implemented segmentation models was based on already established fully convolutional neural network designs from the literature 24, 25 . Fully convolutional networks following an encoder decoder architecture both in 2D and 3D were developed and evaluated. For the 2D models the CT scans were separated on the axial view. The network included 5 convolutional blocks, each one containing two Conv-BN-ReLU layer successions. Maxpooling layers were also distributed at the end of each convolutional block for the encoding part. Transposed convolutions were used on the decoding part to restore the spatial resolution of the slices together with the same successions of layers. For the 3D pipeline, the model similarly consisted of five blocks with a down-sampling operation applied every two consequent Conv3D-BN-ReLU layers. Additionally, five decoding blocks were utilized for the decoding path, at each block a transpose convolution was performed in order to up-sample the input. Skip connections were also employed between the encoding and decoding paths. In order to train this model, cubic patches of size 64 × 64 × 64 were randomly extracted within a close range of the ground truth annotation border in a random fashion. Corresponding cubic patches were also extracted from the ground truth annotation masks and the lung anatomy segmentation masks. To this end, we trained the model with the CT scan patch as input, the annotation patch as target and the lung anatomy annotation patch as a mask for calculating the loss function only within the lung region. In order to train all the models, each CT scan was normalized by cropping the Hounsfield units in the range [−1024, 1000].