Fully automatic deep learning-based lung parenchyma segmentation and boundary correction in thoracic CT scans

ConclusionsUsing a CNN model, the proposed method for lung parenchyma segmentation produced precise segmentation results. Furthermore, the post-processing algorithm addressed false positives and negatives in the model ’s predictions. Overall, the proposed approach demonstrated promising results for lung parenchyma segmentation. The method has the potential to be valuable in the advancement of computer-aided diagnosis (CAD) systems for automatic nodule detection.
Source: International Journal of Computer Assisted Radiology and Surgery - Category: Intensive Care Source Type: research