2D-3D Deformable Image Registration of Histology Slide and Micro-CT with ML-based Initialization
This work addresses a specific problem in medical imaging for pathology applications, such as virtual histology, by improving registration accuracy between histology and micro-CT, though it appears incremental as it builds on existing registration techniques with ML enhancements.
The paper tackles the challenge of aligning 2D histology slides with 3D micro-CT images, which is difficult due to soft tissue deformation and low CT quality, by proposing a novel 2D-3D multi-modal deformable image registration method with ML-based initialization and out-of-plane refinement, achieving superior performance over intensity- and keypoint-based methods in evaluations on tonsil and tumor datasets.
Recent developments in the registration of histology and micro-computed tomography (μCT) have broadened the perspective of pathological applications such as virtual histology based on μCT. 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 μCT. 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. μCTs 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.