Analyzing huge pathology images with open source software

TL;DR

This paper introduces open source software tools that enable efficient processing and analysis of large digital pathology images on standard computers, overcoming hardware and format limitations.

Contribution

The authors developed cross-platform open source tools for converting, mosaicing, and analyzing huge pathology images, improving accessibility and performance over existing solutions.

Findings

Tools successfully handle multi-gigabyte images on average computers.

Performance tests show rapid execution and low RAM usage.

Enables detailed cell analysis in large tissue samples.

Abstract

Backgr: Digital pathology images are increasingly used both for diagnosis and research, because slide scanners are nowadays broadly available and because the quantitative study of these images yields new insights in systems biology. However, such virtual slides build up a technical challenge since the images occupy often several gigabytes and cannot be fully opened in a computer's memory. Moreover, there is no standard format. Therefore, most common open source tools such as ImageJ fail at treating them, and the others require expensive hardware while still being prohibitively slow. Res: We have developed several cross-platform open source software tools to overcome these limitations. The NDPITools provide a way to transform microscopy images initially in the loosely supported NDPI format into one or several standard TIFF files, and to create mosaics (division of huge images into small ones, with or without overlap) in various TIFF and JPEG formats. They can be driven through ImageJ plugins. The LargeTIFFTools achieve similar functionality for huge TIFF images which do not fit into RAM. We test the performance of these tools on several digital slides and compare them, when applicable, to standard software. A statistical study of the cells in a tissue sample from an oligodendroglioma was performed on an average laptop computer to demonstrate the efficiency of the tools. Concl: Our open source software enables dealing with huge images with standard software on average computers. Our tools are cross-platform, independent of proprietary libraries, and very modular, allowing them to be used in other open source projects. They have excellent performance in terms of execution speed and RAM requirements. They open promising perspectives both to the clinician who wants to study a single slide and to the research team or data centre who do image analysis of many slides on a computer cluster.