Multi-Scale Component-Trees for Enhanced Representation in Multiplex Immunohistochemistry Imaging

Multiplex immunohistochemistry (mIHC) is an innovative method that simultaneously labels multiple biomarkers in the same tissue section with different colored stains. However, analyzing these complex images is a challenging task for current image processing methods. In order to efficiently process these images, it may be beneficial to employ an effective representation of the data. The component-tree is known to facilitate the representation of images containing sparse objects, which necessitate representation at a higher scale, as opposed to large background regions that can be represented at a lower scale without loss of fidelity. In this paper, we present how a multi-scale version of the component-tree can be effectively utilized to represent this type of data, drawing inspiration from the approach a pathologist might take when analyzing these images (initially identifying objects of interest via a specific nuclear channel, such as DAPI, and then associating each object with a spectral signature based on the intensities of other channels). We demonstrate this method on an unsupervised analysis of 9-channel multiplexed glioblastoma data.