A fully automated micro‑CT deep learning approach for precision preclinical investigation of lung fibrosis progression and response to therapy.

ABSTRACT: Micro-computed tomography (µCT)-based imaging plays a key role in monitoring disease progression and response to candidate drugs in various animal models of human disease, but manual image processing is still highly time-consuming and prone to operator bias. Focusing on an established mouse model of bleomycin (BLM)-induced lung fibrosis we document, here, the ability of a fully automated deep-learning (DL)-based model to improve and speed-up lung segmentation and the precise measurement of morphological and functional biomarkers in both the whole lung and in individual lobes. µCT-DL whose results were overall highly consistent with those of more conventional, especially histological, analyses, allowed to cut down by approximately 45-fold the time required to analyze the entire dataset and

SUBMITTER: Buccardi M 

PROVIDER: S-EPMC10170869 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature