TIAToolbox as an end-to-end library for advanced tissue image analytics

• Published in: Communications medicine Vol. 2; no. 1; pp. 120 - 14
• Main Authors: Pocock, Johnathan, Graham, Simon, Vu, Quoc Dang, Jahanifar, Mostafa, Deshpande, Srijay, Hadjigeorghiou, Giorgos, Shephard, Adam, Bashir, Raja Muhammad Saad, Bilal, Mohsin, Lu, Wenqi, Epstein, David, Minhas, Fayyaz, Rajpoot, Nasir M., Raza, Shan E Ahmed
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.09.2022; Nature Portfolio
• Subjects: 13; 14/63; 631/114; 631/67/2321; Medicine; Medicine & Public Health; Cancer imaging; Computational biology and bioinformatics
• ISSN: 2730-664X; 2730-664X
• DOI: 10.1038/s43856-022-00186-5

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. Plain language summary Computational software is being introduced to pathology, the study of the causes and effects of disease. Recently various computational pathology algorithms have been developed to analyze digital histology images. However, the software code written for these algorithms often combines functionality from several software packages which have specific setup requirements and code styles. This makes it difficult to re-use this code in other projects. We developed a computational software named TIAToolbox to alleviate this problem and hope this will help accelerate the use of computational software in pathology. Pocock, Graham et al. present TIAToolbox, a Python toolbox for computational pathology. The extendable library can be used for data loading, pre-processing, model inference, post-processing, and visualization.