Open Access 01-12-2021 | Computed Tomography | Technical advance
COVID-19 lung CT image segmentation using deep learning methods: U-Net versus SegNet
Published in: BMC Medical Imaging | Issue 1/2021
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Background
SegNet
and U-NET
, are investigated for semantically segmenting infected tissue regions in CT lung images.Methods
SegNet
and U-NET
, for image tissue classification. SegNet
is characterized as a scene segmentation network and U-NET
as a medical segmentation tool. Both networks were exploited as binary segmentors to discriminate between infected and healthy lung tissue, also as multi-class segmentors to learn the infection type on the lung. Each network is trained using seventy-two data images, validated on ten images, and tested against the left eighteen images. Several statistical scores are calculated for the results and tabulated accordingly.Results
SegNet
in classifying infected/non-infected tissues compared to the other methods (with 0.95 mean accuracy), while the U-NET
shows better results as a multi-class segmentor (with 0.91 mean accuracy).