Persistent homology analysis distinguishes pathological bone microstructure in non-linear microscopy images

Abstract

We present a topological method for the detection and quantification of bone microstructure from non-linear microscopy images. Specifically, we analyse second harmonic generation (SHG) and two photon excited autofluorescence (TPaF) images of bone tissue which capture the distribution of matrix (fibrillar collagen) structure and autofluorescent molecules, respectively. Using persistent homology statistics with a signed Euclidean distance transform filtration on binary patches of images, we are able to quantify the number, size, distribution, and crowding of holes within and across samples imaged at the microscale. We apply our methodology to a previously characterized murine model of skeletal pathology whereby vascular endothelial growth factor expression was deleted in osteocalcin-expressing cells (OcnVEGFKO) presenting increased cortical porosity, compared to wild type (WT) littermate controls. We show significant differences in topological statistics between the OcnVEGFKO and WT groups and, when classifying the males, or females respectively, into OcnVEGFKO or WT groups, we obtain high prediction accuracies of 98.7% (74.2%) and 77.8% (65.8%) respectively for SHG (TPaF) images. The persistence statistics that we use are fully interpretable, can highlight regions of abnormality within an image and identify features at different spatial scales.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

* We have made changes are to the introductory paragraphs (refined definition of micro-holes, one paragraph has moved to the methods, one paragraph has been simplified). We have also amended the statements we make about the interpretability of the results. In our article, we mean interpretability in the sense of data science, namely in contrast to AI/machine learning black box approaches. In our topological method, each persistent statistic can be linked directly to a morphological interpretation, as in Table 1. We have rewritten the conclusion to better summarise the method and include the implications for bone analysis.