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

arXiv: https://arxiv.org/abs/2208.08343 · HTML: https://mlsanity.com/paper/2208.08343/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 22 references that are in the map.
- First posted on arXiv 25 Jul 2022.

## Builds on (most cited first, 3 of 3)

- [Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images](https://mlsanity.com/paper/2004.14133/) (2020)
- [MosMedData: Chest CT Scans With COVID-19 Related Findings Dataset](https://mlsanity.com/paper/2005.06465/) (2020)
- [Evaluating Transferability for Covid 3D Localization Using CT SARS-CoV-2 segmentation models](https://mlsanity.com/paper/2205.02152/) (2022)

## Abstract (arXiv)

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.