A Pair of Inception Blocks in U-Net Architecture for Lung Segmentation

2021 ◽  
Author(s):  
Ade Nurhopipah ◽  
Aris Wahyu Murdiyanto ◽  
Tri Astuti
Keyword(s):  
Lung Segmentation

scholarly journals Multi-stage transfer learning for lung segmentation using portable X-ray devices for patients with COVID-19

2021 ◽  
Vol 173 ◽  
pp. 114677
Author(s):  
Plácido L. Vidal ◽  
Joaquim de Moura ◽  
Jorge Novo ◽  
Marcos Ortega
Keyword(s):  
Transfer Learning ◽  
Lung Segmentation ◽  
X Ray ◽  
Multi Stage

scholarly journals Deep learning-based segmentation of the lung in MR-images acquired by a stack-of-spirals trajectory at ultra-short echo-times

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Andreas M. Weng ◽  
Julius F. Heidenreich ◽  
Corona Metz ◽  
Simon Veldhoen ◽  
Thorsten A. Bley ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Deep Learning ◽  
Accurate Estimation ◽  
Dice Similarity Coefficient ◽  
Manual Segmentation ◽  
Pearson’S Correlation ◽  
Lung Volumes ◽  
Lung Segmentation ◽  
Pearson's Correlation ◽  
Lung Mri

Abstract Background Functional lung MRI techniques are usually associated with time-consuming post-processing, where manual lung segmentation represents the most cumbersome part. The aim of this study was to investigate whether deep learning-based segmentation of lung images which were scanned by a fast UTE sequence exploiting the stack-of-spirals trajectory can provide sufficiently good accuracy for the calculation of functional parameters. Methods In this study, lung images were acquired in 20 patients suffering from cystic fibrosis (CF) and 33 healthy volunteers, by a fast UTE sequence with a stack-of-spirals trajectory and a minimum echo-time of 0.05 ms. A convolutional neural network was then trained for semantic lung segmentation using 17,713 2D coronal slices, each paired with a label obtained from manual segmentation. Subsequently, the network was applied to 4920 independent 2D test images and results were compared to a manual segmentation using the Sørensen–Dice similarity coefficient (DSC) and the Hausdorff distance (HD). Obtained lung volumes and fractional ventilation values calculated from both segmentations were compared using Pearson’s correlation coefficient and Bland Altman analysis. To investigate generalizability to patients outside the CF collective, in particular to those exhibiting larger consolidations inside the lung, the network was additionally applied to UTE images from four patients with pneumonia and one with lung cancer. Results The overall DSC for lung tissue was 0.967 ± 0.076 (mean ± standard deviation) and HD was 4.1 ± 4.4 mm. Lung volumes derived from manual and deep learning based segmentations as well as values for fractional ventilation exhibited a high overall correlation (Pearson’s correlation coefficent = 0.99 and 1.00). For the additional cohort with unseen pathologies / consolidations, mean DSC was 0.930 ± 0.083, HD = 12.9 ± 16.2 mm and the mean difference in lung volume was 0.032 ± 0.048 L. Conclusions Deep learning-based image segmentation in stack-of-spirals based lung MRI allows for accurate estimation of lung volumes and fractional ventilation values and promises to replace the time-consuming step of manual image segmentation in the future.

scholarly journals Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem

2020 ◽  
Vol 4 (1) ◽  
Author(s):  
Johannes Hofmanninger ◽  
Forian Prayer ◽  
Jeanny Pan ◽  
Sebastian Röhrich ◽  
Helmut Prosch ◽  
...  
Keyword(s):  
Lung Segmentation ◽  
Routine Imaging

scholarly journals Lung segmentation in digital radiographs

1994 ◽  
Vol 7 (2) ◽  
pp. 79-84 ◽  
Author(s):  
Ewa Pietka
Keyword(s):  
Lung Segmentation ◽  
Digital Radiographs

Merging parametric active contours within homogeneous image regions for MRI-based lung segmentation

2003 ◽  
Vol 22 (2) ◽  
pp. 189-199 ◽  
Author(s):  
N. Ray ◽  
S.T. Acton ◽  
T. Altes ◽  
E.E. de Lange ◽  
J.R. Brookeman
Keyword(s):  
Active Contours ◽  
Lung Segmentation ◽  
Parametric Active Contours

scholarly journals A Visually Guided Framework for Lung Segmentation and Visualization in Chest CT Images

2018 ◽  
Vol 8 (3) ◽  
pp. 485-493 ◽  
Author(s):  
Shouren Lan ◽  
Xin Liu ◽  
Lisheng Wang ◽  
Chaoyi Cui
Keyword(s):  
Ct Images ◽  
Chest Ct ◽  
Visually Guided ◽  
Lung Segmentation
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