Enhanced vasculature visualization in OCT images of tumor model grown subcutaneously in dorsal window chambers

Monitoring tumor blood vessels during growth, disease progression, and post-treatment can provide valuable diagnostic information and advance knowledge about tumors and their microenvironment. This work aimed to develop an enhanced visualization technique for tumor models grown subcutaneously in dorsal window chambers (DWC) and non-invasively imaged with speckle variance optical coherence tomography (SV-OCT).

We developed a multi-step image processing pipeline for enhanced vasculature visualization. It starts with Gaussian filtering to reduce image noise, followed by a vessel enhancement filter analyzing the eigenvalues of the Hessian matrix to identify vessel-like structures. Finally, the depth of vessels in the DWC was determined through maximal SV-OCT intensity. This pipeline was applied to a series of SV-OCT images of 4T1 murine models during tumor growth and after electroporation-based therapy.

The novel image processing pipeline allowed us to qualitatively assess tumor vasculature development, topography, and effective depth in DWC. The series of vasculature images shows vessel growth over time and regression during therapy. We demonstrated that SV-OCT combined with suitable image preprocessing can be used to monitor vasculature development in DWC tumor models.