Automated segmentation of lungs and lung tumors in mouse micro-CT scans - Ferl, Gregory Z.; Barck, Kai H.; Patil, Jasmine; Jemaa, Skander; Malamut, Evelyn J.; Lima, Anthony; Long, Jason E.; Cheng, Jason H.; Junttila, Melissa R.; Carano, Richard A.D. (iScience, 2022)
Here, we have developed an automated image processing algorithm for segmenting lungs and individual lung tumors in in vivo micro-computed tomography (micro-CT) scans of mouse models of non-small cell lung cancer and lung fibrosis. Over 3000 scans acquired across multiple studies were used to train/validate a 3D U-net lung segmentation model and a Support Vector Machine (SVM) classifier to segment individual lung tumors. The U-net lung segmentation algorithm can be used to estimate changes in soft tissue volume within lungs (primarily tumors and blood vessels), whereas the trained SVM is able to discriminate between tumors and blood vessels and identify individual tumors. The trained segmentation algorithms (1) significantly reduce time required for lung and tumor segmentation, (2) reduce bias and error associated with manual image segmentation, and (3) facilitate identification of individual lung tumors and objective assessment of changes in lung and individual tumor volumes under different experimental conditions. [Display omitted] •Manually segmenting lungs/tumors in murine CT images is subjective and time consuming•Automated algorithm segments lungs and identifies individual lung tumors•Automated algorithm reduces bias and image processing time•Facilitates translational investigation of intra-subject tumor heterogeneity Cancer; Artificial intelligence; Machine learning