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Article

Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies

1
Ri.MED Foundation, 90133 Palermo, Italy
2
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), 90015 Cefalù, Italy
3
Department of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
4
Department of Medical Surgical Sciences and Advanced Technologies, Radiology Unit I, University Hospital “Policlinico-Vittorio Emanuele”, 95123 Catania, Italy
5
Regional Referral Centre for Rare Lung Diseases, A.O.U. Policlinico-Vittorio Emanuele, University of Catania, 95123 Catania, Italy
*
Author to whom correspondence should be addressed.
Received: 6 October 2020 / Revised: 11 November 2020 / Accepted: 18 November 2020 / Published: 19 November 2020
(This article belongs to the Special Issue Radiomics and Texture Analysis in Medical Imaging)
Background: The aim of this work is to identify an automatic, accurate, and fast deep learning segmentation approach, applied to the parenchyma, using a very small dataset of high-resolution computed tomography images of patients with idiopathic pulmonary fibrosis. In this way, we aim to enhance the methodology performed by healthcare operators in radiomics studies where operator-independent segmentation methods must be used to correctly identify the target and, consequently, the texture-based prediction model. Methods: Two deep learning models were investigated: (i) U-Net, already used in many biomedical image segmentation tasks, and (ii) E-Net, used for image segmentation tasks in self-driving cars, where hardware availability is limited and accurate segmentation is critical for user safety. Our small image dataset is composed of 42 studies of patients with idiopathic pulmonary fibrosis, of which only 32 were used for the training phase. We compared the performance of the two models in terms of the similarity of their segmentation outcome with the gold standard and in terms of their resources’ requirements. Results: E-Net can be used to obtain accurate (dice similarity coefficient = 95.90%), fast (20.32 s), and clinically acceptable segmentation of the lung region. Conclusions: We demonstrated that deep learning models can be efficiently applied to rapidly segment and quantify the parenchyma of patients with pulmonary fibrosis, without any radiologist supervision, in order to produce user-independent results. View Full-Text
Keywords: idiopathic pulmonary fibrosis; deep learning; lung segmentation; high resolution computed tomography; radiomics; U-Net; E-Net idiopathic pulmonary fibrosis; deep learning; lung segmentation; high resolution computed tomography; radiomics; U-Net; E-Net
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MDPI and ACS Style

Comelli, A.; Coronnello, C.; Dahiya, N.; Benfante, V.; Palmucci, S.; Basile, A.; Vancheri, C.; Russo, G.; Yezzi, A.; Stefano, A. Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies. J. Imaging 2020, 6, 125. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110125

AMA Style

Comelli A, Coronnello C, Dahiya N, Benfante V, Palmucci S, Basile A, Vancheri C, Russo G, Yezzi A, Stefano A. Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies. Journal of Imaging. 2020; 6(11):125. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110125

Chicago/Turabian Style

Comelli, Albert; Coronnello, Claudia; Dahiya, Navdeep; Benfante, Viviana; Palmucci, Stefano; Basile, Antonio; Vancheri, Carlo; Russo, Giorgio; Yezzi, Anthony; Stefano, Alessandro. 2020. "Lung Segmentation on High-Resolution Computerized Tomography Images Using Deep Learning: A Preliminary Step for Radiomics Studies" J. Imaging 6, no. 11: 125. https://0-doi-org.brum.beds.ac.uk/10.3390/jimaging6110125

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