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

• URL: http://arxiv.org/abs/2208.08343v1
• Date: Mon, 25 Jul 2022 10:43:26 GMT
• Title: Transferability limitations for Covid 3D Localization Using SARS-CoV-2 segmentation models in 4D CT images
• Authors: Constantine Maganaris, Eftychios Protopapadakis, Nikolaos Bakalos, Nikolaos Doulamis, Dimitris Kalogeras, Aikaterini Angeli
• Abstract summary: We investigate the transferability limitations when using deep learning models, for semantic segmentation of pneumonia-infected areas in CT images. Experimental results suggest that transferability should be used carefully, when creating Covid segmentation models.
• Score: 8.026717228180935
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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