Abstract
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 suggest 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.
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Acknowledgements
This research has been co-financed by European Union’s Horizon 2020 research and innovation programme under grant agreement No. 883441 for the STAMINA Innovation action.
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Maganaris, C., Protopapadakis, E., Bakalos, N., Doulamis, N., Kalogeras, D., Angeli, A. (2022). Transferability Limitations for Covid 3D Localization Using SARS-CoV-2 Segmentation Models in 4D CT Images. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13599. Springer, Cham. https://doi.org/10.1007/978-3-031-20716-7_25
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