Accurate and Robust Alignment of Differently Stained Histologic Images Based on Greedy Diffeomorphic Registration
| Autor: | Spyridon Bakas, Ludovic Venet, Paul A. Yushkevich, Michael Feldman, Sarthak Pati, MacLean Nasrallah |
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| Rok vydání: | 2021 |
| Předmět: |
Computer science
Radiography deformable Image registration lcsh:Technology Article lcsh:Chemistry diffeomorphic histology 03 medical and health sciences 0302 clinical medicine Robustness (computer science) registration General Materials Science Disease process Instrumentation lcsh:QH301-705.5 030304 developmental biology Fluid Flow and Transfer Processes 0303 health sciences business.industry lcsh:T Process Chemistry and Technology General Engineering Digital pathology Pattern recognition lcsh:QC1-999 Computer Science Applications Anatomical sites lcsh:Biology (General) lcsh:QD1-999 lcsh:TA1-2040 histopathology Diffeomorphism Affine transformation Artificial intelligence ANHIR challenge business lcsh:Engineering (General). Civil engineering (General) digital pathology 030217 neurology & neurosurgery lcsh:Physics |
| Zdroj: | Applied sciences (Basel, Switzerland) Applied Sciences Volume 11 Issue 4 Applied Sciences, Vol 11, Iss 1892, p 1892 (2021) |
| ISSN: | 2076-3417 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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