COVID-19 CT image segmentation based on improved Res2Net.

Med Phys

School of Computer and Information Engineering, Henan Normal University, Xinxiang, Henan, China.

Published: December 2022

AI Article Synopsis

Article Abstract

Purpose: Corona virus disease 2019 (COVID-19) is threatening the health of the global people and bringing great losses to our economy and society. However, computed tomography (CT) image segmentation can make clinicians quickly identify the COVID-19-infected regions. Accurate segmentation infection area of COVID-19 can contribute screen confirmed cases.

Methods: We designed a segmentation network for COVID-19-infected regions in CT images. To begin with, multilayered features were extracted by the backbone network of Res2Net. Subsequently, edge features of the infected regions in the low-level feature f were extracted by the edge attention module. Second, we carefully designed the structure of the attention position module (APM) to extract high-level feature f and detect infected regions. Finally, we proposed a context exploration module consisting of two parallel explore blocks, which can remove some false positives and false negatives to reach more accurate segmentation results.

Results: Experimental results show that, on the public COVID-19 dataset, the Dice, sensitivity, specificity, , , and mean absolute error (MAE) of our method are 0.755, 0.751, 0.959, 0.795, 0.919, and 0.060, respectively. Compared with the latest COVID-19 segmentation model Inf-Net, the Dice similarity coefficient of our model has increased by 7.3%; the sensitivity (Sen) has increased by 5.9%. On contrary, the MAE has dropped by 2.2%.

Conclusions: Our method performs well on COVID-19 CT image segmentation. We also find that our method is so portable that can be suitable for various current popular networks. In a word, our method can help screen people infected with COVID-19 effectively and save the labor power of clinicians and radiologists.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9538682PMC
http://dx.doi.org/10.1002/mp.15882DOI Listing

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