Part-to-whole Registration of Histology and MRI using Shape Elements

TL;DR

This paper introduces an automatic, contrast-invariant method for registering histology and MRI images by leveraging shape elements and level lines, effectively handling partial tissue samples without prior alignment.

Contribution

The novel approach uses projective-invariant shape elements extracted from level lines to automatically align partial histology and MRI images without prior information.

Findings

Preliminary results show promising alignment accuracy.

Method effectively handles contrast differences and partial tissue samples.

Shape elements improve robustness and efficiency of registration.

Abstract

Image registration between histology and magnetic resonance imaging (MRI) is a challenging task due to differences in structural content and contrast. Too thick and wide specimens cannot be processed all at once and must be cut into smaller pieces. This dramatically increases the complexity of the problem, since each piece should be individually and manually pre-aligned. To the best of our knowledge, no automatic method can reliably locate such piece of tissue within its respective whole in the MRI slice, and align it without any prior information. We propose here a novel automatic approach to the joint problem of multimodal registration between histology and MRI, when only a fraction of tissue is available from histology. The approach relies on the representation of images using their level lines so as to reach contrast invariance. Shape elements obtained via the extraction of bitangents are encoded in a projective-invariant manner, which permits the identification of common pieces of curves between two images. We evaluated the approach on human brain histology and compared resulting alignments against manually annotated ground truths. Considering the complexity of the brain folding patterns, preliminary results are promising and suggest the use of characteristic and meaningful shape elements for improved robustness and efficiency.