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Awan, Ruqayya, Raza, Shan-e-Ahmed, Lotz, Johannes, Weiss, Nick and Rajpoot, Nasir M. (2023) Deep feature based cross-slide registration. Computerized Medical Imaging and Graphics, 104 . 102162. doi:10.1016/j.compmedimag.2022.102162 ISSN 0895-6111.
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WRAP-deep-feature-based-cross-slide-registration-Awan-Rajpoot-2023.pdf - Accepted Version Embargoed item. Restricted access to Repository staff only until 19 December 2023. Contact author directly, specifying your specific needs. - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2268Kb) |
Official URL: https://doi.org/10.1016/j.compmedimag.2022.102162
Abstract
Registration of multiple sections in a tissue block is an important pre-requisite task before any cross-slide image analysis. Non-rigid registration methods are capable of finding correspondence by locally transforming a moving image. These methods often rely on an initial guess to roughly align an image pair linearly and globally. This is essential to prevent convergence to a non-optimal minimum. We explore a deep feature based registration (DFBR) method which utilises data-driven descriptors to estimate the global transformation. A multi-stage strategy is adopted for improving the quality of registration. A visualisation tool is developed to view registered pairs of WSIs at different magnifications. With the help of this tool, one can apply a transformation on the fly without the need to generate a transformed moving WSI in a pyramidal form. We compare the performance on our dataset of data-driven descriptors with that of hand-crafted descriptors. Our approach can align the images with only small registration errors. The efficacy of our proposed method is evaluated for a subsequent non-rigid registration step. To this end, the first two steps of the ANHIR winner's framework are replaced with DFBR to register image pairs provided by the challenge. The modified framework produce comparable results to those of the challenge winning team.
| Item Type: | Journal Article | ||||||||
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| Subjects: | Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) |
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| Divisions: | Faculty of Science, Engineering and Medicine > Science > Computer Science | ||||||||
| SWORD Depositor: | Library Publications Router | ||||||||
| Library of Congress Subject Headings (LCSH): | Image registration, Diagnostic imaging -- Data processing, Image analysis, Machine learning, Medical radiology -- Technique | ||||||||
| Journal or Publication Title: | Computerized Medical Imaging and Graphics | ||||||||
| Publisher: | Pergamon | ||||||||
| ISSN: | 0895-6111 | ||||||||
| Official Date: | March 2023 | ||||||||
| Dates: |
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| Volume: | 104 | ||||||||
| Article Number: | 102162 | ||||||||
| DOI: | 10.1016/j.compmedimag.2022.102162 | ||||||||
| Status: | Peer Reviewed | ||||||||
| Publication Status: | Published | ||||||||
| Access rights to Published version: | Restricted or Subscription Access | ||||||||
| Date of first compliant deposit: | 25 January 2023 |
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