A semi-automatic pipeline integrating histological and µCT data in a mouse model of lung fibrosis
Elena Vincenzi1, Martina Buccardi2,3, Erica Ferrini3,4
1Camelot Biomedical Systems S.R.L, Genoa, Italy.
Background:
Drug discovery strongly relies on the thorough evaluation of preclinical experimental studies. In the context of pulmonary fibrosis, micro-computed tomography (µCT) and histology are well-established and complementary tools for assessing, in animal models, disease progression and response to treatment. µCT offers dynamic, real-time insights into disease evolution and the effects of therapies, while histology provides a detailed microscopic examination of lung tissue. Here, we present a semi-automatic pipeline that integrates these readouts by matching individual µCT volume slices with the corresponding histological sections, effectively linking densitometric data with Ashcroft score measurements.
Methods:
The tool first geometrically aligns the vertical axis of the µCT volume with the cutting plane used to prepare the histological sample. Then, focusing on the left lung, it computes the affine registration that identifies the µCT coronal slice that best matches the histological section. Finally, quantitative µCT imaging parameters are extracted from the selected slice. In a proof-of-concept test, the tool was applied to a bleomycin-induced mouse model of lung fibrosis.
Results:
The proposed approach demonstrated high accuracy and time effectiveness in matching µCT and histological sections minimizing manual intervention, with an overall success rate of 95%, and reduced time required to align µCT and histological data from 40 to 5 min. Significant correlations were found between quantitative data derived from µCT and histology data.
Conclusions:
The precise combination of microscopic ex-vivo information with 3D in-vivo data enhances the accuracy and representativeness of tissue analysis and provides a structural context for omic studies, serving as the foundation for a multi-layer platform. By facilitating a detailed and objective view of disease progression and treatment response, this approach has the potential to accelerate the development of effective therapies for lung fibrosis.
