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Article

Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration

1
Center for Biomedical Image Computing & Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
2
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
3
Department of Pathology & Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
*
Author to whom correspondence should be addressed.
Equally contributing authors.
Academic Editor: Ahmad Chaddad
Appl. Sci. 2021, 11(4), 1892; https://doi.org/10.3390/app11041892
Received: 25 January 2021 / Revised: 16 February 2021 / Accepted: 17 February 2021 / Published: 21 February 2021
(This article belongs to the Special Issue Artificial Intelligence for Personalised Medicine)
Histopathologic assessment routinely provides rich microscopic information about tissue structure and disease process. However, the sections used are very thin, and essentially capture only 2D representations of a certain tissue sample. Accurate and robust alignment of sequentially cut 2D slices should contribute to more comprehensive assessment accounting for surrounding 3D information. Towards this end, we here propose a two-step diffeomorphic registration approach that aligns differently stained histology slides to each other, starting with an initial affine step followed by estimating a deformation field. It was quantitatively evaluated on ample (n = 481) and diverse data from the automatic non-rigid histological image registration challenge, where it was awarded the second rank. The obtained results demonstrate the ability of the proposed approach to robustly (average robustness = 0.9898) and accurately (average relative target registration error = 0.2%) align differently stained histology slices of various anatomical sites while maintaining reasonable computational efficiency (<1 min per registration). The method was developed by adapting a general-purpose registration algorithm designed for 3D radiographic scans and achieved consistently accurate results for aligning high-resolution 2D histologic images. Accurate alignment of histologic images can contribute to a better understanding of the spatial arrangement and growth patterns of cells, vessels, matrix, nerves, and immune cell interactions. View Full-Text
Keywords: registration; deformable; diffeomorphic; digital pathology; histology; histopathology; ANHIR challenge registration; deformable; diffeomorphic; digital pathology; histology; histopathology; ANHIR challenge
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MDPI and ACS Style

Venet, L.; Pati, S.; Feldman, M.D.; Nasrallah, M.P.; Yushkevich, P.; Bakas, S. Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration. Appl. Sci. 2021, 11, 1892. https://doi.org/10.3390/app11041892

AMA Style

Venet L, Pati S, Feldman MD, Nasrallah MP, Yushkevich P, Bakas S. Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration. Applied Sciences. 2021; 11(4):1892. https://doi.org/10.3390/app11041892

Chicago/Turabian Style

Venet, Ludovic, Sarthak Pati, Michael D. Feldman, MacLean P. Nasrallah, Paul Yushkevich, and Spyridon Bakas. 2021. "Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration" Applied Sciences 11, no. 4: 1892. https://doi.org/10.3390/app11041892

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