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TIAToolbox as an end-to-end library for advanced tissue image analytics
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Pocock, Johnathan, Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Deshpande, Srijay, Hadjigeorghiou, Giorgos, Shephard, Adam, Bashir, Raja Muhammad Saad, Bilal, Mohsin, Lu, Wenqi, Epstein, D. B. A., Minhas, Fayyaz ul Amir Afsar, Rajpoot, Nasir M. and Raza, Shan-e-Ahmed (2022) TIAToolbox as an end-to-end library for advanced tissue image analytics. Communications Medicine, 2 (1). 120. doi:10.1038/s43856-022-00186-5 ISSN 2730-664X.
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WRAP-TIAToolbox-as-an-end-to-end-library-advanced-tissue-image-analytics-2022.pdf - Published Version - Requires a PDF viewer. Available under License Creative Commons Attribution 4.0. Download (3824Kb) | Preview |
Official URL: https://doi.org/10.1038/s43856-022-00186-5
Abstract
Background: Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers. Methods: By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models. Results: We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort. Conclusions: We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature.
| Item Type: | Journal Article | ||||||
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| Subjects: | R Medicine > RB Pathology R Medicine > RC Internal medicine |
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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): | Pathology -- Data processing, Histology, Pathological -- Methodology, Diagnostic imaging -- Digital techniques, Pathology -- Technological innovations, Machine learning | ||||||
| Journal or Publication Title: | Communications Medicine | ||||||
| Publisher: | Nature Publishing Group UK | ||||||
| ISSN: | 2730-664X | ||||||
| Official Date: | 24 September 2022 | ||||||
| Dates: |
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| Volume: | 2 | ||||||
| Number: | 1 | ||||||
| Article Number: | 120 | ||||||
| DOI: | 10.1038/s43856-022-00186-5 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 10 October 2022 | ||||||
| Date of first compliant Open Access: | 11 October 2022 |
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