  Tuberculosis (TB) is still considered a leading cause of death and a
substantial threat to global child health. Both TB infection and disease are
curable using antibiotics. However, most children who die of TB are never
diagnosed or treated. In clinical practice, experienced physicians assess TB by
examining chest X-rays (CXR). Pediatric CXR has specific challenges compared to
adult CXR, which makes TB diagnosis in children more difficult. Computer-aided
diagnosis systems supported by Artificial Intelligence have shown performance
comparable to experienced radiologist TB readings, which could ease mass TB
screening and reduce clinical burden. We propose a multi-view deep
learning-based solution which, by following a proposed template, aims to
automatically regionalize and extract lung and mediastinal regions of interest
from pediatric CXR images where key TB findings may be present. Experimental
results have shown accurate region extraction, which can be used for further
analysis to confirm TB finding presence and severity assessment. Code publicly
available at https://github.com/dani-capellan/pTB_LungRegionExtractor.
