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In this paper, we investigate the transferability limitations when using deeplearning models, for semantic segmentation of pneumonia-infected areas in CTimages. The proposed approach adopts a 4 channel input; 3 channels based onHounsfield scale, plus one channel (binary) denoting the lung area. We used 3different, publicly available, CT datasets. If the lung area mask was notavailable, a deep learning model generates a proxy image. Experimental resultssuggesting that transferability should be used carefully, when creating Covidsegmentation models; retraining the model more than one times in large sets ofdata results in a decrease in segmentation accuracy.

Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images