  In this paper, we investigate the transferability limitations when using deep
learning models, for semantic segmentation of pneumonia-infected areas in CT
images. The proposed approach adopts a 4 channel input; 3 channels based on
Hounsfield scale, plus one channel (binary) denoting the lung area. We used 3
different, publicly available, CT datasets. If the lung area mask was not
available, a deep learning model generates a proxy image. Experimental results
suggesting that transferability should be used carefully, when creating Covid
segmentation models; retraining the model more than one times in large sets of
data results in a decrease in segmentation accuracy.
