Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts
This solves the challenge of 3D tissue analysis in medical imaging by enabling more accurate volume reconstruction, though it is incremental as it builds on regional registration approaches.
The paper tackles the problem of accurately reconstructing 3D tissue volumes from 2D whole slide image stacks by addressing distortions from cutting and mounting, proposing a regional registration algorithm that incrementally focuses on areas of interest. The result shows it outperforms state-of-the-art methods, achieving a mean similarity index of 0.84 ± 0.11, with further improvement to 0.86 ± 0.08 after fine registration.
Motivation: High resolution 2D whole slide imaging provides rich information about the tissue structure. This information can be a lot richer if these 2D images can be stacked into a 3D tissue volume. A 3D analysis, however, requires accurate reconstruction of the tissue volume from the 2D image stack. This task is not trivial due to the distortions that each individual tissue slice experiences while cutting and mounting the tissue on the glass slide. Performing registration for the whole tissue slices may be adversely affected by the deformed tissue regions. Consequently, regional registration is found to be more effective. In this paper, we propose an accurate and robust regional registration algorithm for whole slide images which incrementally focuses registration on the area around the region of interest. Results: Using mean similarity index as the metric, the proposed algorithm (mean $\pm$ std: $0.84 \pm 0.11$) followed by a fine registration algorithm ($0.86 \pm 0.08$) outperformed the state-of-the-art linear whole tissue registration algorithm ($0.74 \pm 0.19$) and the regional version of this algorithm ($0.81 \pm 0.15$). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original : $0.82 \pm 0.12$, regional : $0.77 \pm 0.22$) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: $0.79 \pm 0.16$ , patch size 512: $0.77 \pm 0.16$) for medical images. Availability: The C++ implementation code is available online at the github repository: https://github.com/MahsaPaknezhad/WSIRegistration