  Chest X-rays (CXRs) are the most commonly performed diagnostic examination to
detect cardiopulmonary abnormalities. However, the presence of bony structures
such as ribs and clavicles can obscure subtle abnormalities resulting in
diagnostic errors. This study aims to build a deep learning (DL)-based bone
suppression model that identifies and removes these occluding bony structures
in frontal CXRs to assist in reducing errors in radiological interpretation,
including DL workflows, related to detecting manifestations consistent with
Tuberculosis (TB). Several bone suppression models with various deep
architectures are trained and their performances are evaluated in a
cross-institutional test setting. The best-performing model (ResNet-BS) is used
to suppress bones in the Shenzhen and Montgomery TB CXR collections. A VGG-16
model is pretrained on a large collection of publicly available CXRs. The
CXR-pretrained model is then fine-tuned individually on the non-bone-suppressed
and bone-suppressed CXRs of Shenzhen and Montgomery TB CXR collections to
classify them as showing normal lungs or TB manifestations. The performances of
these models are compared using several performance metrics, analyzed for
statistical significance, and their predictions are qualitatively interpreted
through class-selective relevance maps (CRM). It is observed that the models
trained on bone-suppressed CXRs significantly outperformed the models trained
individually on the non-bone-suppressed CXRs (p<0.05) in the Shenzhen and
Montgomery TB collections. Models trained on bone-suppressed CXRs improved
detection of TB-consistent findings and resulted in compact clustering of the
data points in the feature space signifying that bone suppression improved the
model sensitivity toward TB classification.
