Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks

Anthimopoulos, Marios; Christodoulidis, Stergios; Ebner, Lukas; Geiser, Thomas; Christe, Andreas; Mougiakakou, Stavroula Georgia (2018). Semantic Segmentation of Pathological Lung Tissue with Dilated Fully Convolutional Networks. IEEE journal of biomedical and health informatics, 23(2), pp. 714-722. Institute of Electrical and Electronics Engineers 10.1109/JBHI.2018.2818620

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Early and accurate diagnosis of interstitial lung diseases (ILDs) is crucial for making treatment decisions, but can be challenging even for experienced radiologists. The diagnostic procedure is based on the detection and recognition of the different ILD pathologies in thoracic CT scans, yet their manifestation often appears similar. In this study, we propose the use of a deep purely convolutional neural network for the semantic segmentation of ILD patterns, as the basic component of a computer aided diagnosis (CAD) system for ILDs. The proposed CNN, which consists of convolutional layers with dilated filters, takes as input a lung CT image of arbitrary size and outputs the corresponding label map. We trained and tested the network on a dataset of 172 sparsely annotated CT scans, within a cross-validation scheme. The training was performed in an end-to-end and semi-supervised fashion, utilizing both labeled and non-labeled image regions. The experimental results show significant performance improvement with respect to the state of the art.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research > ARTORG Center - Diabetes Technology
04 Faculty of Medicine > Department of Gastro-intestinal, Liver and Lung Disorders (DMLL) > Clinic of Pneumology
04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology
10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Anthimopoulos, Marios; Christodoulidis, Stergios; Ebner, Lukas; Geiser, Thomas; Christe, Andreas and Mougiakakou, Stavroula Georgia

Subjects:

600 Technology > 610 Medicine & health
500 Science > 570 Life sciences; biology
600 Technology > 620 Engineering

ISSN:

2168-2194

Publisher:

Institute of Electrical and Electronics Engineers

Language:

English

Submitter:

Stavroula Mougiakakou

Date Deposited:

26 Apr 2018 15:58

Last Modified:

17 Mar 2019 02:29

Publisher DOI:

10.1109/JBHI.2018.2818620

Additional Information:

M. Anthimopoulos and S. Christodoulidis contributed equally to this work.

BORIS DOI:

10.7892/boris.114795

URI:

https://boris.unibe.ch/id/eprint/114795

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