Regional Registration of Whole Slide Image Stacks Containing Highly Deformed Artefacts
Mahsa Paknezhad, Sheng Yang Michael Loh, Yukti Choudhury, Valerie Koh Cui Koh, Timothy Kwang Yong Tay, Hui Shan Tan, Ravindran Kanesvaran, Puay Hoon Tan, John Yuen Shyi Peng, Weimiao Yu, Yongcheng Benjamin Tan, Yong Zhen Loy, Min Han Tan, Hwee Kuan Lee · Research Square · 2020
Abstract Background: 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 +- std: 0.84 +- 0.11) followed by a fine registration algorithm (0.86 +- 0.08) outperformed the state-of-the-art linear whole tissue registration algorithm (0.74 +- 0.19) and the regional version of this algorithm (0.81 +- 0.15). The proposed algorithm also outperforms the state-of-the-art nonlinear registration algorithm (original: 0.82 +- 0.12, regional: 0.77 +- 0.22) for whole slide images and a recently proposed patch-based registration algorithm (patch size 256: 0.79 +- 0.16 , patch size 512: 0.77 +- 0.16) for medical images. Conclusion: The proposed algorithm is a more robust and accurate solution to the problem of regional registration of whole slide images in existence of highly deformed regions in the imaged tissue.