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Authors: M. E. Fantacci 1 ; A. Traverso 2 ; S. Bagnasco 3 ; C. Bracco 4 ; D. Campanella 4 ; G. Chiara 4 ; E. Lopez Torres 5 ; A. Manca 4 ; D. Regge 4 ; M. Saletta 3 ; M. Stasi 4 ; S. Vallero 3 ; L. Vassallo 4 and P. Cerello 3

Affiliations: 1 University of Pisa and Sezione di Pisa, Italy ; 2 Polytechnic University of Turin and Sezione di Torino, Italy ; 3 Sezione di Torino, Italy ; 4 Candiolo Cancer Institute - FPO and IRCCS, Italy ; 5 Sezione di Torino and CEADEN, Italy

ISBN: 978-989-758-214-1

Keyword(s): Web Service, Cloud Computing, Computer Aided Detection, Lung Nodules.

Related Ontology Subjects/Areas/Topics: Algorithms and Software Tools ; Bioinformatics ; Biomedical Engineering ; Image Analysis ; Pattern Recognition, Clustering and Classification ; Web Services in Bioinformatics

Abstract: M5L, a Web-based Computer-Aided Detection (CAD) system to automatically detect lung nodules in thoracic Computed Tomographies, is based on a multi-thread analysis by independent subsystems and the combination of their results. The validation on 1043 scans of 3 independent data-sets showed consistency across data-sets, with a sensitivity of about 80% in the 4-8 range of False Positives per scan, despite varying acquisition and reconstruction parameters and annotation criteria. To make M5L CAD available to users without hardware or software new installations and configuration, a Software as a Service (SaaS) approach was adopted. A web front-end handles the work (image upload, results notification and direct on-line annotation by radiologists) and the communication with the OpenNebula-based cloud infrastructure, that allocates virtual computing and storage resources. The exams uploaded through the web interface are anonymised and analysis is performed in an isolated and independent cloud environment. The average processing time for case is about 20 minutes and up to 14 cases can be processed in parallel. Preliminary results on the on-going clinical validation shows that the M5L CAD adds 20% more nodules originally overlooked by radiologists, allowing a remarkable increase of the overall detection sensitivity. (More)

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Paper citation in several formats:
Fantacci M., Traverso A., Bagnasco S., Bracco C., Campanella D., Chiara G., Torres E., Manca A., Regge D., Saletta M., Stasi M., Vallero S., Vassallo L. and Cerello P. (2017). A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies).In Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017) ISBN 978-989-758-214-1, pages 202-209. DOI: 10.5220/0006245402020209

@conference{bioinformatics17,
author={M. E. Fantacci and A. Traverso and S. Bagnasco and C. Bracco and D. Campanella and G. Chiara and E. Lopez Torres and A. Manca and D. Regge and M. Saletta and M. Stasi and S. Vallero and L. Vassallo and P. Cerello},
title={A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies)},
booktitle={Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)},
year={2017},
pages={202-209},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006245402020209},
isbn={978-989-758-214-1},
}

TY - CONF

JO - Proceedings of the 10th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 3: BIOINFORMATICS, (BIOSTEC 2017)
TI - A Web- and Cloud- based Service for the Clinical Use of a CAD (Computer Aided Detection) System - Automated Detection of Lung Nodules in Thoracic CTs (Computed Tomographies)
SN - 978-989-758-214-1
AU - Fantacci M.
AU - Traverso A.
AU - Bagnasco S.
AU - Bracco C.
AU - Campanella D.
AU - Chiara G.
AU - Torres E.
AU - Manca A.
AU - Regge D.
AU - Saletta M.
AU - Stasi M.
AU - Vallero S.
AU - Vassallo L.
AU - Cerello P.
PY - 2017
SP - 202
EP - 209
DO - 10.5220/0006245402020209

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