DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.
Rajaraman S, Cohen G, Spear L, Folio L, Antani S
PLOS ONE 17(3): e0265691. https://doi.org/10.1371/journal.pone.0265691
Abstract:
Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and the clavicles. Automated bone suppression methods would increase soft tissue visibility and enhance automated dis- ease detection. We evaluate this hypothesis using a custom ensemble of convolutional neu- ral network models, which we call DeBoNet, that suppresses bones in frontal CXRs. First, we train and evaluate variants of U-Nets, Feature Pyramid Networks, and other proposed custom models using a private collection of CXR images and their bone-suppressed coun- terparts. The DeBoNet, constructed using the top-3 performing models, outperformed the individual models in terms of peak signal-to-noise ratio (PSNR) (36.7977±1.6207), multi- scale structural similarity index measure (MS-SSIM) (0.9848±0.0073), and other metrics. Next, the best-performing bone-suppression model is applied to CXR images that are pooled from several sources, showing no abnormality and other findings consistent with COVID-19. The impact of bone suppression is demonstrated by evaluating the gain in per- formance in detecting pulmonary abnormality consistent with COVID-19 disease. We observe that the model trained on bone-suppressed CXRs (MCC: 0.9645, 95% confidence interval (0.9510, 0.9780)) significantly outperformed (p < 0.05) the model trained on non- bone-suppressed images (MCC: 0.7961, 95% confidence interval (0.7667, 0.8255)) in detecting findings consistent with COVID-19 indicating benefits derived from automatic bone suppression on disease classification. The code is available at https://github.com/ sivaramakrishnan-rajaraman/Bone-Suppresion-Ensemble.
Rajaraman S, Cohen G, Spear L, Folio L, Antani S. DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.
PLOS ONE 17(3): e0265691. https://doi.org/10.1371/journal.pone.0265691
URL: https://doi.org/10.1371/journal.pone.0265691