Improved TB classification using bone-suppressed chest radiographs

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.