作者
L Berta, Francesco Rizzetto, Cristina De Mattia, Domenico Lizio, Marco Felisi, Paola Enrica Colombo, Stefano Carrazza, Stefania Gelmini, L Bianchi, D Artioli, Francesca Travaglini, Alberto Vanzulli, Alberto Torresin, Niguarda COVID-19 Working Group
发表日期
2021/7/1
期刊
Physica Medica
卷号
87
页码范围
115-122
出版商
Elsevier
简介
Purpose
To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.
Methods
Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Four radiologists assessed the segmentations using a 5-point qualitative score (QS). For each CT series, a manually revised reference segmentation (RS) was obtained. Histogram-based quantitative metrics (QM) were calculated from CT histogram using lung segmentationsfrom all platforms and RS. Dice index (DI) and differences of QMs (ΔQMs) were calculated between RS and other segmentations.
Results
Highest QS and lower ΔQMs values were associated to the CNN algorithm. However, only 45% CNN …
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L Berta, F Rizzetto, C De Mattia, D Lizio, M Felisi… - Physica Medica, 2021