Mixture of Deep-Learning Experts for Separation of Bones from Soft Tissue in Chest Radiographs

Lung nodules that overlap with ribs and clavicles in chest radiographs can be difficult to be detected by radiologists as well as by computer-aided detection systems. Removing bony structures would result in better visualization of undetectable lesions. Our purpose in this study was to develop a deep-learning scheme to separate ribs and clavicles from soft tissue in chest radiographs. To achieve this, we developed a mixture of anatomy-specific, orientation-frequency-specific (ASOFS) deep neural network convolution (NNC) experts. Anatomy-specific (AS) architecture was designed to separate bony structures from soft tissue in different lung segments. The orientation-frequency-specific (OFS) was designed to decompose bone and soft-tissue structures into specific orientation-frequency components of different scales using a multi-resolution decomposition technique. For evaluation, we used a database of 118 chest radiographs with pulmonary nodules. Quantitative and qualitative evaluation showed that our ASOFS NNC was superior to a state-of-the-art bone-suppression technique. Particularly, our NNC scheme separated ribs and clavicles from soft tissue, while it was better able to maintain the conspicuity of lung nodules and vessels, comparing to the reference technique. Therefore, our deep-learning scheme can be useful for radiologists as well as CAD systems in detection of lung nodules in chest radiographs.