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

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

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...

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Bibliographic Details
Main Authors: Albert Comelli, Claudia Coronnello, Navdeep Dahiya, Viviana Benfante, Stefano Palmucci, Antonio Basile, Carlo Vancheri, Giorgio Russo, Anthony Yezzi, Alessandro Stefano
Format: Article
Language:English
Published: MDPI AG 2020-11-01
Series:Journal of Imaging
Subjects:
idiopathic pulmonary fibrosis
deep learning
lung segmentation
high resolution computed tomography
radiomics
U-Net
Online Access:https://www.mdpi.com/2313-433X/6/11/125

Internet

https://www.mdpi.com/2313-433X/6/11/125

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