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Scapicchio C, Chincarini A, Ballante E, Berta L, Bicci E, Bortolotto C, Brero F, Cabini RF, Cristofalo G, Fanni SC, Fantacci ME, Figini S, Galia M, Gemma P, Grassedonio E, Lascialfari A, Lenardi C, Lionetti A, Lizzi F, Marrale M, Midiri M, Nardi C, Oliva P, Perillo N, Postuma I, Preda L, Rastrelli V, Rizzetto F, Spina N, Talamonti C, Torresin A, Vanzulli A, Volpi F, Neri E, Retico A. A multicenter evaluation of a deep learning software (LungQuant) for lung parenchyma characterization in COVID-19 pneumonia. Eur Radiol Exp 2023; 7:18. [PMID: 37032383 PMCID: PMC10083148 DOI: 10.1186/s41747-023-00334-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Accepted: 02/27/2023] [Indexed: 04/11/2023] Open
Abstract
BACKGROUND The role of computed tomography (CT) in the diagnosis and characterization of coronavirus disease 2019 (COVID-19) pneumonia has been widely recognized. We evaluated the performance of a software for quantitative analysis of chest CT, the LungQuant system, by comparing its results with independent visual evaluations by a group of 14 clinical experts. The aim of this work is to evaluate the ability of the automated tool to extract quantitative information from lung CT, relevant for the design of a diagnosis support model. METHODS LungQuant segments both the lungs and lesions associated with COVID-19 pneumonia (ground-glass opacities and consolidations) and computes derived quantities corresponding to qualitative characteristics used to clinically assess COVID-19 lesions. The comparison was carried out on 120 publicly available CT scans of patients affected by COVID-19 pneumonia. Scans were scored for four qualitative metrics: percentage of lung involvement, type of lesion, and two disease distribution scores. We evaluated the agreement between the LungQuant output and the visual assessments through receiver operating characteristics area under the curve (AUC) analysis and by fitting a nonlinear regression model. RESULTS Despite the rather large heterogeneity in the qualitative labels assigned by the clinical experts for each metric, we found good agreement on the metrics compared to the LungQuant output. The AUC values obtained for the four qualitative metrics were 0.98, 0.85, 0.90, and 0.81. CONCLUSIONS Visual clinical evaluation could be complemented and supported by computer-aided quantification, whose values match the average evaluation of several independent clinical experts. KEY POINTS We conducted a multicenter evaluation of the deep learning-based LungQuant automated software. We translated qualitative assessments into quantifiable metrics to characterize coronavirus disease 2019 (COVID-19) pneumonia lesions. Comparing the software output to the clinical evaluations, results were satisfactory despite heterogeneity of the clinical evaluations. An automatic quantification tool may contribute to improve the clinical workflow of COVID-19 pneumonia.
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Affiliation(s)
- Camilla Scapicchio
- Physics Department, University of Pisa, Pisa, Italy.
- Pisa Division, National Institute for Nuclear Physics, Pisa, Italy.
| | - Andrea Chincarini
- Genova Division, National Institute for Nuclear Physics, Genova, Italy
| | - Elena Ballante
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- Pavia Division, National Institute for Nuclear Physics, Pavia, Italy
| | - Luca Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Milano Division, National Institute for Nuclear Physics, Milan, Italy
| | - Eleonora Bicci
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Chandra Bortolotto
- Unit of Imaging and Radiotherapy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
- Institute of Radiology, Department of Diagnostic and Imaging Services, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Francesca Brero
- Pavia Division, National Institute for Nuclear Physics, Pavia, Italy
| | - Raffaella Fiamma Cabini
- Pavia Division, National Institute for Nuclear Physics, Pavia, Italy
- Department of Mathematics, University of Pavia, Pavia, Italy
| | - Giuseppe Cristofalo
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Palermo, Italy
| | | | - Maria Evelina Fantacci
- Physics Department, University of Pisa, Pisa, Italy
- Pisa Division, National Institute for Nuclear Physics, Pisa, Italy
| | - Silvia Figini
- Department of Political and Social Sciences, University of Pavia, Pavia, Italy
- Pavia Division, National Institute for Nuclear Physics, Pavia, Italy
| | - Massimo Galia
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Palermo, Italy
| | - Pietro Gemma
- Post-graduate School in Radiodiagnostics, University of Milan, Milan, Italy
| | - Emanuele Grassedonio
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Palermo, Italy
| | | | - Cristina Lenardi
- Milano Division, National Institute for Nuclear Physics, Milan, Italy
- Department of Physics "Aldo Pontremoli", University of Milan, Milan, Italy
| | - Alice Lionetti
- Unit of Imaging and Radiotherapy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
| | - Francesca Lizzi
- Physics Department, University of Pisa, Pisa, Italy
- Pisa Division, National Institute for Nuclear Physics, Pisa, Italy
| | - Maurizio Marrale
- Department of Physics and Chemistry "Emilio Segrè", University of Palermo, Palermo, Italy
- Catania Division, National Institute for Nuclear Physics, Catania, Italy
| | - Massimo Midiri
- Department of Biomedicine, Neuroscience and Advanced Diagnostic (BiND), University of Palermo, Palermo, Italy
| | - Cosimo Nardi
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Piernicola Oliva
- Cagliari Division, National Institute for Nuclear Physics, Monserrato, Cagliari, Italy
- Department of Chemical, Physical, Mathematical and Natural Sciences, University of Sassari, Sassari, Italy
| | - Noemi Perillo
- Post-graduate School in Radiodiagnostics, University of Milan, Milan, Italy
| | - Ian Postuma
- Pavia Division, National Institute for Nuclear Physics, Pavia, Italy
| | - Lorenzo Preda
- Unit of Imaging and Radiotherapy, Department of Clinical-Surgical, Diagnostic and Pediatric Sciences, University of Pavia, Pavia, Italy
- Institute of Radiology, Department of Diagnostic and Imaging Services, Fondazione IRCCS Policlinico San Matteo, Pavia, Italy
| | - Vieri Rastrelli
- Department of Experimental and Clinical Biomedical Sciences, Radiodiagnostic Unit n. 2, University of Florence-Azienda Ospedaliero-Universitaria Careggi, Florence, Italy
| | - Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Postgraduate School of Diagnostic and Interventional Radiology, University of Milan, Milan, Italy
| | - Nicola Spina
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Cinzia Talamonti
- Department Biomedical Experimental and Clinical Science "Mario Serio", University of Florence, Florence, Italy
- Florence Division, National Institute for Nuclear Physics, Sesto Fiorentino, Firenze, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Milano Division, National Institute for Nuclear Physics, Milan, Italy
- Department of Physics "Aldo Pontremoli", University of Milan, Milan, Italy
| | - Angelo Vanzulli
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Federica Volpi
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
| | - Emanuele Neri
- Department of Translational Research, Academic Radiology, University of Pisa, Pisa, Italy
- Italian Society of Medical and Interventional Radiology, SIRM Foundation, Milan, Italy
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Hsia CCW, Bates JHT, Driehuys B, Fain SB, Goldin JG, Hoffman EA, Hogg JC, Levin DL, Lynch DA, Ochs M, Parraga G, Prisk GK, Smith BM, Tawhai M, Vidal Melo MF, Woods JC, Hopkins SR. Quantitative Imaging Metrics for the Assessment of Pulmonary Pathophysiology: An Official American Thoracic Society and Fleischner Society Joint Workshop Report. Ann Am Thorac Soc 2023; 20:161-195. [PMID: 36723475 PMCID: PMC9989862 DOI: 10.1513/annalsats.202211-915st] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/02/2023] Open
Abstract
Multiple thoracic imaging modalities have been developed to link structure to function in the diagnosis and monitoring of lung disease. Volumetric computed tomography (CT) renders three-dimensional maps of lung structures and may be combined with positron emission tomography (PET) to obtain dynamic physiological data. Magnetic resonance imaging (MRI) using ultrashort-echo time (UTE) sequences has improved signal detection from lung parenchyma; contrast agents are used to deduce airway function, ventilation-perfusion-diffusion, and mechanics. Proton MRI can measure regional ventilation-perfusion ratio. Quantitative imaging (QI)-derived endpoints have been developed to identify structure-function phenotypes, including air-blood-tissue volume partition, bronchovascular remodeling, emphysema, fibrosis, and textural patterns indicating architectural alteration. Coregistered landmarks on paired images obtained at different lung volumes are used to infer airway caliber, air trapping, gas and blood transport, compliance, and deformation. This document summarizes fundamental "good practice" stereological principles in QI study design and analysis; evaluates technical capabilities and limitations of common imaging modalities; and assesses major QI endpoints regarding underlying assumptions and limitations, ability to detect and stratify heterogeneous, overlapping pathophysiology, and monitor disease progression and therapeutic response, correlated with and complementary to, functional indices. The goal is to promote unbiased quantification and interpretation of in vivo imaging data, compare metrics obtained using different QI modalities to ensure accurate and reproducible metric derivation, and avoid misrepresentation of inferred physiological processes. The role of imaging-based computational modeling in advancing these goals is emphasized. Fundamental principles outlined herein are critical for all forms of QI irrespective of acquisition modality or disease entity.
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Zorzi G, Berta L, Rizzetto F, De Mattia C, Felisi MMJ, Carrazza S, Nerini Molteni S, Vismara C, Scaglione F, Vanzulli A, Torresin A, Colombo PE. Artificial intelligence for differentiating COVID-19 from other viral pneumonias on CT: comparative analysis of different models based on quantitative and radiomic approaches. Eur Radiol Exp 2023; 7:3. [PMID: 36690869 PMCID: PMC9870776 DOI: 10.1186/s41747-022-00317-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Accepted: 12/15/2022] [Indexed: 01/25/2023] Open
Abstract
BACKGROUND To develop a pipeline for automatic extraction of quantitative metrics and radiomic features from lung computed tomography (CT) and develop artificial intelligence (AI) models supporting differential diagnosis between coronavirus disease 2019 (COVID-19) and other viral pneumonia (non-COVID-19). METHODS Chest CT of 1,031 patients (811 for model building; 220 as independent validation set (IVS) with positive swab for severe acute respiratory syndrome coronavirus-2 (647 COVID-19) or other respiratory viruses (384 non-COVID-19) were segmented automatically. A Gaussian model, based on the HU histogram distribution describing well-aerated and ill portions, was optimised to calculate quantitative metrics (QM, n = 20) in both lungs (2L) and four geometrical subdivisions (GS) (upper front, lower front, upper dorsal, lower dorsal; n = 80). Radiomic features (RF) of first (RF1, n = 18) and second (RF2, n = 120) order were extracted from 2L using PyRadiomics tool. Extracted metrics were used to develop four multilayer-perceptron classifiers, built with different combinations of QM and RF: Model1 (RF1-2L); Model2 (QM-2L, QM-GS); Model3 (RF1-2L, RF2-2L); Model4 (RF1-2L, QM-2L, GS-2L, RF2-2L). RESULTS The classifiers showed accuracy from 0.71 to 0.80 and area under the receiving operating characteristic curve (AUC) from 0.77 to 0.87 in differentiating COVID-19 versus non-COVID-19 pneumonia. Best results were associated with Model3 (AUC 0.867 ± 0.008) and Model4 (AUC 0.870 ± 0.011. For the IVS, the AUC values were 0.834 ± 0.008 for Model3 and 0.828 ± 0.011 for Model4. CONCLUSIONS Four AI-based models for classifying patients as COVID-19 or non-COVID-19 viral pneumonia showed good diagnostic performances that could support clinical decisions.
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Affiliation(s)
- Giulia Zorzi
- Postgraduate School of Medical Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Luca Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Francesco Rizzetto
- Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy.
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy.
| | - Cristina De Mattia
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Marco Maria Jacopo Felisi
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
| | - Stefano Carrazza
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Silvia Nerini Molteni
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Chiara Vismara
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
| | - Francesco Scaglione
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, via Festa del Perdono 7, 20122, Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, INFN Sezione di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
| | - Paola Enrica Colombo
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162, Milan, Italy
- Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133, Milan, Italy
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Topff L, Sánchez-García J, López-González R, Pastor AJ, Visser JJ, Huisman M, Guiot J, Beets-Tan RGH, Alberich-Bayarri A, Fuster-Matanzo A, Ranschaert ER. A deep learning-based application for COVID-19 diagnosis on CT: The Imaging COVID-19 AI initiative. PLoS One 2023; 18:e0285121. [PMID: 37130128 PMCID: PMC10153726 DOI: 10.1371/journal.pone.0285121] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Accepted: 04/15/2023] [Indexed: 05/03/2023] Open
Abstract
BACKGROUND Recently, artificial intelligence (AI)-based applications for chest imaging have emerged as potential tools to assist clinicians in the diagnosis and management of patients with coronavirus disease 2019 (COVID-19). OBJECTIVES To develop a deep learning-based clinical decision support system for automatic diagnosis of COVID-19 on chest CT scans. Secondarily, to develop a complementary segmentation tool to assess the extent of lung involvement and measure disease severity. METHODS The Imaging COVID-19 AI initiative was formed to conduct a retrospective multicentre cohort study including 20 institutions from seven different European countries. Patients with suspected or known COVID-19 who underwent a chest CT were included. The dataset was split on the institution-level to allow external evaluation. Data annotation was performed by 34 radiologists/radiology residents and included quality control measures. A multi-class classification model was created using a custom 3D convolutional neural network. For the segmentation task, a UNET-like architecture with a backbone Residual Network (ResNet-34) was selected. RESULTS A total of 2,802 CT scans were included (2,667 unique patients, mean [standard deviation] age = 64.6 [16.2] years, male/female ratio 1.3:1). The distribution of classes (COVID-19/Other type of pulmonary infection/No imaging signs of infection) was 1,490 (53.2%), 402 (14.3%), and 910 (32.5%), respectively. On the external test dataset, the diagnostic multiclassification model yielded high micro-average and macro-average AUC values (0.93 and 0.91, respectively). The model provided the likelihood of COVID-19 vs other cases with a sensitivity of 87% and a specificity of 94%. The segmentation performance was moderate with Dice similarity coefficient (DSC) of 0.59. An imaging analysis pipeline was developed that returned a quantitative report to the user. CONCLUSION We developed a deep learning-based clinical decision support system that could become an efficient concurrent reading tool to assist clinicians, utilising a newly created European dataset including more than 2,800 CT scans.
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Affiliation(s)
- Laurens Topff
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | | | | | | | - Jacob J Visser
- Department of Radiology and Nuclear Medicine, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands
| | - Merel Huisman
- Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Julien Guiot
- Department of Pneumology, University Hospital of Liège (CHU Liège), Liège, Belgium
| | - Regina G H Beets-Tan
- Department of Radiology, Netherlands Cancer Institute, Amsterdam, The Netherlands
- GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | | | | | - Erik R Ranschaert
- Department of Radiology, St. Nikolaus Hospital, Eupen, Belgium
- Ghent University, Ghent, Belgium
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5
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Rizzetto F, Berta L, Zorzi G, Cincotta A, Travaglini F, Artioli D, Nerini Molteni S, Vismara C, Scaglione F, Torresin A, Colombo PE, Carbonaro LA, Vanzulli A. Diagnostic Performance in Differentiating COVID-19 from Other Viral Pneumonias on CT Imaging: Multi-Reader Analysis Compared with an Artificial Intelligence-Based Model. Tomography 2022; 8:2815-2827. [PMID: 36548527 PMCID: PMC9785796 DOI: 10.3390/tomography8060235] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/19/2022] [Accepted: 11/21/2022] [Indexed: 11/29/2022] Open
Abstract
Growing evidence suggests that artificial intelligence tools could help radiologists in differentiating COVID-19 pneumonia from other types of viral (non-COVID-19) pneumonia. To test this hypothesis, an R-AI classifier capable of discriminating between COVID-19 and non-COVID-19 pneumonia was developed using CT chest scans of 1031 patients with positive swab for SARS-CoV-2 (n = 647) and other respiratory viruses (n = 384). The model was trained with 811 CT scans, while 220 CT scans (n = 151 COVID-19; n = 69 non-COVID-19) were used for independent validation. Four readers were enrolled to blindly evaluate the validation dataset using the CO-RADS score. A pandemic-like high suspicion scenario (CO-RADS 3 considered as COVID-19) and a low suspicion scenario (CO-RADS 3 considered as non-COVID-19) were simulated. Inter-reader agreement and performance metrics were calculated for human readers and R-AI classifier. The readers showed good agreement in assigning CO-RADS score (Gwet's AC2 = 0.71, p < 0.001). Considering human performance, accuracy = 78% and accuracy = 74% were obtained in the high and low suspicion scenarios, respectively, while the AI classifier achieved accuracy = 79% in distinguishing COVID-19 from non-COVID-19 pneumonia on the independent validation dataset. The R-AI classifier performance was equivalent or superior to human readers in all comparisons. Therefore, a R-AI classifier may support human readers in the difficult task of distinguishing COVID-19 from other types of viral pneumonia on CT imaging.
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Affiliation(s)
- Francesco Rizzetto
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
- Correspondence:
| | - Luca Berta
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Giulia Zorzi
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Postgraduate School of Medical Physics, Università degli Studi di Milano, Via Giovanni Celoria 16, 20133 Milan, Italy
| | - Antonino Cincotta
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Postgraduate School of Diagnostic and Interventional Radiology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Francesca Travaglini
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Diana Artioli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Silvia Nerini Molteni
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Chiara Vismara
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
| | - Francesco Scaglione
- Chemical-Clinical and Microbiological Analyses, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Alberto Torresin
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Department of Physics, Università degli Studi di Milano, Via Giovanni Celoria 16, 20133 Milan, Italy
| | - Paola Enrica Colombo
- Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Department of Physics, Università degli Studi di Milano, Via Giovanni Celoria 16, 20133 Milan, Italy
| | - Luca Alessandro Carbonaro
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
| | - Angelo Vanzulli
- Department of Radiology, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy
- Department of Oncology and Hemato-Oncology, Università degli Studi di Milano, Via Festa del Perdono 7, 20122 Milan, Italy
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Wang G, Guo S, Han L, Cekderi AB. Two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm for lung parenchyma segmentation of COVID-19 CT image. Biomed Signal Process Control 2022; 78:103933. [PMID: 35774106 PMCID: PMC9217142 DOI: 10.1016/j.bspc.2022.103933] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2022] [Revised: 05/28/2022] [Accepted: 06/18/2022] [Indexed: 12/01/2022]
Abstract
The lesions of COVID-19 CT image show various kinds of ground-glass opacity and consolidation, which are distributed in left lung, right lung or both lungs. The lung lobes are uneven and it have similar gray value to the surrounding arteries, veins, and bronchi. The lesions of COVID-19 have different sizes and shapes in different periods. Accurate segmentation of lung parenchyma in CT image is a key step in COVID-19 detection and diagnosis. Aiming at the unideal effect of traditional image segmentation methods on lung parenchyma segmentation in CT images, a lung parenchyma segmentation method based on two-dimensional reciprocal cross entropy multi-threshold combined with improved firefly algorithm is proposed. Firstly, the optimal threshold method is used to realize the initial segmentation of the lung, so that the segmentation threshold can change adaptively according to the detailed information of lung lobes, trachea, bronchi and ground-glass opacity. Then the lung parenchyma is further processed to obtain the lung parenchyma template, and then the defective template is repaired combined with the improved Freeman chain code and Bezier curve. Finally, the lung parenchyma is extracted by multiplying the template with the lung CT image. The accuracy of lung parenchyma segmentation has been improved in the contrast clarity of CT image and the consistency of lung parenchyma regional features, with an average segmentation accuracy rate of 97.4%. The experimental results show that for COVID-19 and suspected cases, the method has an ideal segmentation effect, and it has good accuracy and robustness.
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Affiliation(s)
- Guowei Wang
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Shuli Guo
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
| | - Lina Han
- Department of Cardiology, The Second Medical Center, National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Anil Baris Cekderi
- State Key Laboratory of Intelligent Control and Decision of Complex Systems, School of Automation, Beijing Institute of Technology, Beijing 100081, China
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Mori M, Alborghetti L, Palumbo D, Broggi S, Raspanti D, Rovere Querini P, Del Vecchio A, De Cobelli F, Fiorino C. Atlas-Based Lung Segmentation Combined With Automatic Densitometry Characterization In COVID-19 Patients: Training, Validation And First Application In A Longitudinal Study. Phys Med 2022; 100:142-152. [PMID: 35839667 PMCID: PMC9250926 DOI: 10.1016/j.ejmp.2022.06.018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 06/15/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose To develop and validate an automated segmentation tool for COVID-19 lung CTs. To combine it with densitometry information in identifying Aerated, Intermediate and Consolidated Volumes in admission (CT1) and follow up CT (CT3). Materials and Methods An Atlas was trained on manually segmented CT1 of 250 patients and validated on 10 CT1 of the training group, 10 new CT1 and 10 CT3, by comparing DICE index between automatic (AUTO), automatic-corrected (AUTOMAN) and manual (MAN) contours. A previously developed automatic method was applied on HU lung density histograms to quantify Aerated, Intermediate and Consolidated Volumes. Volumes of subregions in validation CT1 and CT3 were quantified for each method. Results In validation CT1/CT3, manual correction of automatic contours was not necessary in 40% of cases. Mean DICE values for both lungs were 0.94 for AUTOVsMAN and 0.96 for AUTOMANVsMAN. Differences between Aerated and Intermediate Volumes quantified with AUTOVsMAN contours were always < 6%. Consolidated Volumes showed larger differences (mean: −95 ± 72 cc). If considering AUTOMANVsMAN volumes, differences got further smaller for Aerated and Intermediate, and were drastically reduced for consolidated Volumes (mean: −36 ± 25 cc). The average time for manual correction of automatic lungs contours on CT1 was 5 ± 2 min. Conclusions An Atlas for automatic segmentation of lungs in COVID-19 patients was developed and validated. Combined with a previously developed method for lung densitometry characterization, it provides a fast, operator-independent way to extract relevant quantitative parameters with minimal manual intervention.
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Affiliation(s)
- Martina Mori
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy.
| | - Lisa Alborghetti
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | - Diego Palumbo
- Radiology, San Raffaele Scientific Institute, Milano, Italy
| | - Sara Broggi
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
| | | | - Patrizia Rovere Querini
- Internal Medecine, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | | | - Francesco De Cobelli
- Radiology, San Raffaele Scientific Institute, Milano, Italy; Faculty of Medicine and Surgery, Vita-Salute San Raffaele University, Milano, Italy
| | - Claudio Fiorino
- Medical Physics, San Raffaele Scientific Institute, Milano, Italy
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