OpenSlide Java 0.12.4 includes minor build and test improvements.
OpenSlide now provides a Fedora Copr, enabling users of Fedora and RHEL-compatible enterprise Linux to easily install the latest OpenSlide and OpenSlide Python releases before they reach Fedora or EPEL.
OpenSlide 4.0.0 adds support for DICOM WSI slides, ICC color profiles, tile
cache customization, adds the
slidetool command-line utility, removes
deprecated APIs, and improves format compatibility.
Windows build 20231011 integrates all dependencies into the OpenSlide DLL,
replaces the separate command-line tools with
slidetool, and switches
from MSVCRT to the
Universal C Runtime
OpenSlide now provides an Ubuntu PPA, enabling Ubuntu users to easily install the latest OpenSlide and OpenSlide Python releases before they reach Ubuntu.
OpenSlide Python 1.3.1 updates the docs and example tools to transform images to sRGB using the default rendering intent of the image’s ICC profile, rather than absolute colorimetric intent.
OpenSlide Python 1.3.0 adds support for the upcoming OpenSlide 4.0.0 and drops support for Python 3.7. It also exposes color management profiles where available, and updates the Deep Zoom example tools to transform images to sRGB by default.
Windows build 20230414 integrates most dependencies into the OpenSlide DLL, and also updates various dependencies.
Windows build 20221217 updates OpenSlide Java and several dependencies.
OpenSlide Java 0.12.3 adds a Meson build system, deprecates the Autotools+Ant one, and fixes builds on newer JDKs.
Older news is available here.
OpenSlide is a C library that provides a simple interface to read whole-slide images (also known as virtual slides). The current version is 4.0.0, released 2023-10-11.
Python and Java bindings are also available. The Python binding includes a Deep Zoom generator and a simple web-based viewer. The Java binding includes a simple image viewer.
OpenSlide and its official language bindings are released under the terms of the GNU Lesser General Public License, version 2.1.
The library can read virtual slides in the following formats:
It provides a simple C interface for programmers to use to decode images of these kinds.
See how some projects use OpenSlide.
There is a web-based demo of OpenSlide rendering various slide formats.
First, try the search box at the top of the page. It covers the OpenSlide website, mailing list, issue tracker, and wiki.
There are two mailing lists for OpenSlide:
Some developer documentation is available:
Development of OpenSlide happens on GitHub:
Some freely-distributable test data is available.
The design and implementation of the library are described in a published technical note:
OpenSlide: A Vendor-Neutral Software Foundation for Digital Pathology Adam Goode, Benjamin Gilbert, Jan Harkes, Drazen Jukic, M. Satyanarayanan Journal of Pathology Informatics 2013, 4:27 Abstract HTML Get PDF
There is also an older technical report:
Whole-slide images, also known as virtual slides, are large, high resolution images used in digital pathology. Reading these images using standard image tools or libraries is a challenge because these tools are typically designed for images that can comfortably be uncompressed into RAM or a swap file. Whole-slide images routinely exceed RAM sizes, often occupying tens of gigabytes when uncompressed. Additionally, whole-slide images are typically multi-resolution, and only a small amount of image data might be needed at a particular resolution.
There is no universal data format for whole-slide images, so each vendor implements its own formats, libraries, and viewers. Vendors typically do not document their formats. Even when there is documentation, important details are omitted. Because a vendor’s library or viewer is the only way to view a particular whole-slide image, doctors and researchers can be unnecessarily tied to a particular vendor. Finally, few (if any) vendors provide libraries and viewers for non-Windows platforms. Some have gone with a server approach, pushing tiles through a web server, or using Java applets, but these approaches have shortcomings in high-latency or non-networked environments.
Development of DICOM and ICC functionality was supported by NCI Imaging Data Commons and has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, under Task Order No. HHSN26110071 under Contract No. HHSN261201500003l.