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.
|Title of host publication||ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging|
|Publisher||IEEE Computer Society|
|Number of pages||4|
|Publication status||Published - Apr 2019|
|Event||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 - Venice, Italy|
Duration: 8 Apr 2019 → 11 Apr 2019
|Name||Proceedings - International Symposium on Biomedical Imaging|
|Conference||16th IEEE International Symposium on Biomedical Imaging, ISBI 2019|
|Period||8/04/19 → 11/04/19|
Bibliographical noteFunding 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.
© 2019 IEEE.
- CT scan
- Multi-task learning
- Radiologist reports
- Weighted loss by uncertainty