The Tissue Image Analytics Toolbox (TIAToolbox) helps to standardise and accelerate the development of automated AI analysis pipelines by providing a wide range of functionalities — from reading whole slide images to running artificial intelligence (AI) models.

During surgery or biopsy, suspicious or diseased tissue is removed, fixed in paraffin or frozen before being sliced into very thin tissue sections. These sections are stained, placed on glass slides, and examined under a microscope by a pathologist. The pathologist then advises clinicians on their diagnosis, which is crucial for selecting appropriate treatment and prognostication if needed.

Over the last decade, this process has been transitioning to the use of digital slide scanners, which produce high-resolution Whole Slide Images (WSIs), typically consisting of billions of pixels per slide. Among other benefits, such as technology in education and seamless requesting of second or third opinions, digitisation of pathology has also contributed to a 10–15% gain in reporting via streamlined diagnostic workflows¹.

The need for this transition was identified due to the increasing number of cases and sections to analyse over the years². Coupled with varying degrees of case complexity, this has resulted in a significant increase in the workload of an already stretched pathologist workforce. According to the latest Royal College of Pathology workforce survey, only 3% of NHS hospitals in the UK report adequate staffing³. Therefore, there is a need to automate the analysis of WSIs to aid pathologists in performing diagnoses.

It is common to ‘reinvent the wheel’ or write monolithic, use-case-specific code with inappropriate quality checks in place when implementing Computational Pathology (CPath) pipelines. A major aim of TIAToolbox is to make it easy for researchers to reuse and adapt existing pipelines. TIAToolbox is built from sturdy and reliable components, with each component having clearly specified inputs and outputs. It uses a modular design to reduce code complexity, making the code easier to understand and maintain. Furthermore, it enables advanced users to easily modify or replace components.

The toolbox allows for the development of complex WSI image analyses by providing robust support for simple tasks, such as feeding images to downstream analysis using pre-trained deep learning methods. It provides support for five major components of whole slide image analysis pipelines: data loading, pre-processing, tile-level or localized tissue analysis, whole slide image-level predictive modelling, and visualisation.

If you are interested in learning more about TIAToolbox and how it can help you with your tissue image analysis challenges, check out our paper in Nature Communications Medicine at TIAToolbox as an end-to-end library for advanced tissue image analytics | Communications Medicine (nature.com). Also be sure to visit the GitHub repository at github.com/TissueImageAnalytics/tiatoolbox and give us a star if you find the toolbox useful. Extensive documentation is also available at tia-toolbox.readthedocs.io/. We welcome your feedback and contributions to make TIA Toolbox even better.

References:

(1)Martin, J. Developing a digital future for pathology. 8th Emirates Pathology & Digital Pathology Utilitarian Conference. Online at https://www.youtube.com/watch?v=DAxoiFtGvBA&t=145s (2021)

(2)Pocock, J., Graham, S., Vu, Q.D. et al. TIAToolbox as an end-to-end library for advanced tissue image analytics. Commun Med 2, 120 (2022). https://doi.org/10.1038/s43856-022-00186-5

(3)The Royal College of Pathologists. Meeting pathology demand: Histopathology workforce census. Online at https://www.rcpath.org/uploads/assets/952a934d-2ec3-48c9-a8e6e00fcdca700f/Meeting-Pathology-Demand-Histopathology-Workforce-Census-2018.pdf 3 (2018).