RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge

• URL: http://arxiv.org/abs/2404.13108v2
• Date: Fri, 26 Apr 2024 10:10:52 GMT
• Title: RegWSI: Whole Slide Image Registration using Combined Deep Feature- and Intensity-Based Methods: Winner of the ACROBAT 2023 Challenge
• Authors: Marek Wodzinski, Niccolò Marini, Manfredo Atzori, Henning Müller,
• Abstract summary: A robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain.
• Score: 1.7625447004432986
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The automatic registration of differently stained whole slide images (WSIs) is crucial for improving diagnosis and prognosis by fusing complementary information emerging from different visible structures. It is also useful to quickly transfer annotations between consecutive or restained slides, thus significantly reducing the annotation time and associated costs. Nevertheless, the slide preparation is different for each stain and the tissue undergoes complex and large deformations. Therefore, a robust, efficient, and accurate registration method is highly desired by the scientific community and hospitals specializing in digital pathology. We propose a two-step hybrid method consisting of (i) deep learning- and feature-based initial alignment algorithm, and (ii) intensity-based nonrigid registration using the instance optimization. The proposed method does not require any fine-tuning to a particular dataset and can be used directly for any desired tissue type and stain. The method scored 1st place in the ACROBAT 2023 challenge. We evaluated using three open datasets: (i) ANHIR, (ii) ACROBAT, and (iii) HyReCo, and performed several ablation studies concerning the resolution used for registration and the initial alignment robustness and stability. The method achieves the most accurate results for the ACROBAT dataset, the cell-level registration accuracy for the restained slides from the HyReCo dataset, and is among the best methods evaluated on the ANHIR dataset. The method does not require any fine-tuning to a new datasets and can be used out-of-the-box for other types of microscopic images. The method is incorporated into the DeeperHistReg framework, allowing others to directly use it to register, transform, and save the WSIs at any desired pyramid level. The proposed method is a significant contribution to the WSI registration, thus advancing the field of digital pathology.

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