2D-3D Deformable Image Registration of Histology Slide and Micro-CT with ML-based Initialization

TL;DR

This paper introduces a novel ML-based initialization method for 2D-3D deformable registration of histology slides and micro-CT images, improving accuracy in challenging soft tissue cases with deformation.

Contribution

It presents a new multi-modal registration approach combining ML initialization with analytical refinement, addressing low image quality and tissue deformation issues.

Findings

Superior registration accuracy over existing methods

Effective on datasets from tonsil and tumor tissues

Works with different micro-CT modalities

Abstract

Recent developments in the registration of histology and micro-computed tomography ({\mu}CT) have broadened the perspective of pathological applications such as virtual histology based on {\mu}CT. This topic remains challenging because of the low image quality of soft tissue CT. Additionally, soft tissue samples usually deform during the histology slide preparation, making it difficult to correlate the structures between histology slide and {\mu}CT. In this work, we propose a novel 2D-3D multi-modal deformable image registration method. The method uses a machine learning (ML) based initialization followed by the registration. The registration is finalized by an analytical out-of-plane deformation refinement. The method is evaluated on datasets acquired from tonsil and tumor tissues. {\mu}CTs of both phase-contrast and conventional absorption modalities are investigated. The registration results from the proposed method are compared with those from intensity- and keypoint-based methods. The comparison is conducted using both visual and fiducial-based evaluations. The proposed method demonstrates superior performance compared to the other two methods.