A Novel and Automated Approach to Classify Radiation Induced Lung Tissue Damage on CT Scans.

ABSTRACT: Radiation-induced lung damage (RILD) is a common side effect of radiotherapy (RT). The ability to automatically segment, classify, and quantify different types of lung parenchymal change is essential to uncover underlying patterns of RILD and their evolution over time. A RILD dedicated tissue classification system was developed to describe lung parenchymal tissue changes on a voxel-wise level. The classification system was automated for segmentation of five lung tissue classes on computed tomography (CT) scans that described incrementally increasing tissue density, ranging from normal lung (Class 1) to consolidation (Class 5). For ground truth data generation, we employed a two-stage data annotation approach, akin to active learning. Manual segmentation was used to train a stage one auto-s

SUBMITTER: Szmul A 

PROVIDER: S-EPMC8909378 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature