Paper Info
Title
The Impact of Interstitial Diseases Patterns on Lung CT Segmentation
Abstract
Lung segmentation represents a fundamental step in the development of computer-aided decision systems for the investigation of interstitial lung diseases. In a holistic lung analysis, eliminating background areas from Computed Tomography (CT) images is essential to avoid the inclusion of noise information and spend unnecessary computational resources on non-relevant data. However, the major challenge in this segmentation task relies on the ability of the models to deal with imaging manifestations associated with severe disease. Based on U-net, a general biomedical image segmentation architecture, we proposed a light-weight and faster architecture. In this 2D approach, experiments were conducted with a combination of two publicly available databases to improve the heterogeneity of the training data. Results showed that, when compared to the original U-net, the proposed architecture maintained performance levels, achieving 0.894 +/- 0.060, 4.493 +/- 0.633 and 4.457 +/- 0.628 for DSC, HD and HD-95 metrics, respectively, when using all patients from the ILD database for testing only, while allowing a more efficient computational usage. Quantitative and qualitative evaluations on the ability to cope with high-density lung patterns associated with severe disease were conducted, supporting the idea that more representative and diverse data is necessary to build robust and reliable segmentation tools.
Year
DOI
Venue
2021
10.1109/EMBC46164.2021.9630354
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
Keywords
DocType
Volume
Deep Learning, Lung Segmentation, CT Images, Interstitial Lung Diseases
Conference
2021
ISSN
Citations 
PageRank 
1557-170X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Francisco Silva100.34
Tânia Pereira2248.61
Joana Morgado301.01
Antonio Cunha400.68
Helder P Oliveira501.01