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DOI: 10.1007/s11548-021-02501-2
¤ OpenAccess: Hybrid
This work has “Hybrid” OA status. This means it is free under an open license in a toll-access journal.

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria

Francesca Lizzi,Abramo Agosti,Francesca Brero,Raffaella Fiamma Cabini,Maria Evelina Fantacci,Silvia Figini,A. Lascialfari,Francesco Laruina,P. Oliva,Stefano Piffer,Ian Postuma,Lisa Rinaldi,C. Talamonti,Alessandra Retico

Computer science
Artificial intelligence
Annotation
2021
Abstract Purpose This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-methods. We investigated the effects of using multiple datasets, heterogeneously populated and annotated according to different criteria. Methods We developed an automated analysis pipeline, the LungQuant system, based on a cascade of two U-nets. The first one (U-net $$_1$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>1</mml:mn> </mml:msub> </mml:math> ) is devoted to the identification of the lung parenchyma; the second one (U-net $$_2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msub> <mml:mrow /> <mml:mn>2</mml:mn> </mml:msub> </mml:math> ) acts on a bounding box enclosing the segmented lungs to identify the areas affected by COVID-19 lesions. Different public datasets were used to train the U-nets and to evaluate their segmentation performances, which have been quantified in terms of the Dice Similarity Coefficients. The accuracy in predicting the CT-Severity Score (CT-SS) of the LungQuant system has been also evaluated. Results Both the volumetric DSC (vDSC) and the accuracy showed a dependency on the annotation quality of the released data samples. On an independent dataset (COVID-19-CT-Seg), both the vDSC and the surface DSC (sDSC) were measured between the masks predicted by LungQuant system and the reference ones. The vDSC (sDSC) values of 0.95±0.01 and 0.66±0.13 (0.95±0.02 and 0.76±0.18, with 5 mm tolerance) were obtained for the segmentation of lungs and COVID-19 lesions, respectively. The system achieved an accuracy of 90% in CT-SS identification on this benchmark dataset. Conclusion We analysed the impact of using data samples with different annotation criteria in training an AI-based quantification system for pulmonary involvement in COVID-19 pneumonia. In terms of vDSC measures, the U-net segmentation strongly depends on the quality of the lesion annotations. Nevertheless, the CT-SS can be accurately predicted on independent test sets, demonstrating the satisfactory generalization ability of the LungQuant .
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    Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria” is a paper by Francesca Lizzi Abramo Agosti Francesca Brero Raffaella Fiamma Cabini Maria Evelina Fantacci Silvia Figini A. Lascialfari Francesco Laruina P. Oliva Stefano Piffer Ian Postuma Lisa Rinaldi C. Talamonti Alessandra Retico published in 2021. It has an Open Access status of “hybrid”. You can read and download a PDF Full Text of this paper here.