Tuberculosis lesions in CT images inferred using 3D-CNN and multi-task learning

P. M. Gordaliza, J. J. Vaquero, Sally Sharpe, F. Gleeson, A. Munoz-Barrutia

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Tuberculosis (TB) is an infectious disease that very frequently damage the lungs and that has a high incidence and mortality rate. The longitudinal assessment of pulmonary affectation is a clear need to boost the development of novel drugs and to control the spread of the disease. In this manuscript, we train a computational model able to infer TB manifestations present in each lung lobe of x-ray Computer Tomography (CT) scans by employing the associated radiologist reports as ground truth. We do so instead of using the classical manually delimited segmentation masks. The proposed learning strategy adapts the V-Net model, which allow us to employ full 3D volumes in order to obtain fine grain features. The model is successfully optimized by a novel loss function for multi-task learning. The loss function employs the model uncertainty to weight the regression and binary tasks. Our results are promising with a Root Mean Square Error of 1.14 in the number of nodules and F1-scores above 0.85 for the most prevalent TB lesions (i.e., Conglomerations, cavitations, consolidations, trees in bud) when considering the whole lung.

Original languageEnglish
Title of host publicationISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging
PublisherIEEE Computer Society
Pages294-297
Number of pages4
ISBN (Electronic)9781538636411
DOIs
Publication statusPublished - Apr 2019
Event16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy
Duration: 8 Apr 201911 Apr 2019

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2019-April
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Conference

Conference16th IEEE International Symposium on Biomedical Imaging, ISBI 2019
Country/TerritoryItaly
CityVenice
Period8/04/1911/04/19

Bibliographical note

Funding Information:
The research leading to these results received funding from the Innovative Medicines Initiative (www.imi.europa.eu) Joint Undertaking under grant agreement no. 115337, whose resources comprise funding from EU FP7/2007-2013 and EFPIA companies in kind contribution. This work was partially funded by projects RTC-2015-3772-1, TEC2015-73064-EXP and TEC2016-78052-R from the Spanish Ministry of Economy, Industry and Competitiveness (MEIC), TOPUS S2013/MIT-3024 project from the regional government of Madrid and by the Department of Health, UK.

Publisher Copyright:
© 2019 IEEE.

Keywords

  • CT scan
  • Multi-task learning
  • Radiologist reports
  • Tuberculosis
  • Weighted loss by uncertainty

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