Skip to main content

Organize, visualize, and analyze histology images.

Project description

Organize, visualize, and analyze histology images.

HistomicsUI organizes and manages whole slide image (WSI) files using Girder. It has a dedicated interface to select WSI, add annotations manually, and to run analysis and algorithms on all or parts of images.

Girder provides authentication, access control, and diverse storage options, including using local file systems and Amazon S3. WSI images are read and displayed via the large_image module. Algorithms are containerized using Docker and are run using the slicer_cli_web Girder plugin. These can be run on multiple worker machines via Girder Worker and celery.

A set of common algorithms are provided by HistomicsTK.

Installation

Linux

In linux with Python 3.6 or newer:

Prerequisites:

  • MongoDB must be installed and running.

  • An appropriate version of Python must be installed.

HistomicsUI uses large_image sources to read different image file formats. You need to install appropriate sources for the files that will be used.

# install all sources from the main repo
pip install large-image[sources] --find-links https://girder.github.io/large_image_wheels

or

# install openslide and tiff sources
pip install large-image-source-tiff large-image-source-openslide --find-links https://girder.github.io/large_image_wheels

Now install the histomicsui package, have Girder build its UI, and start the Girder server. Note that at Girder may still require an old version of node (12.x) to build correctly – nvm can be used to manage multiple versions of node.

pip install histomicsui[analysis]
girder build
girder serve

To use Girder Worker:

pip install girder_slicer_cli_web[worker]
GW_DIRECT_PATHS=true girder-worker -l info -Ofair --prefetch-multiplier=1

The first time you start HistomicsUI, you’ll also need to configure Girder with at least one user and one assetstore (see the Girder documentation). Additionally, it is recommended that you install the HistomicsTK algorithms. This can be done going to the Admin Console, Plugins, Slicer CLI Web settings. Set a default task upload folder, then import the dsarchive/histomicstk:latest docker image.

Reference Deployment

The standard deployment of HistomicsUI is the Digital Slide Archive. The associated repository has tools for readily installing via Docker, VirtualBox, or shell scripts on Ubuntu.

Development

The most convenient way to develop on HistomicsUI is to use the devops scripts from the Digital Slide Archive.

If you are making changes to the HistomicsUI frontend, you can make Girder watch the source code and perform hot reloads on changes using the --watch-plugin argument to girder build. See the Girder docs for more information.

Annotations and Metadata from Jobs

This handles ingesting annotations and metadata that are uploaded and associating them with existing large image items in the Girder database. These annotations and metadata re commonly generated through jobs, such as HistomicTK tasks, but can also be added manually.

If a file is uploaded to the Girder system that includes a reference record, and that reference record contains an identifier field and at least one of a fileId and an itemId field, specific identifiers can be used to ingest the results. If a userId is specified in the reference record, permissions for adding the annotation or metadata are associated with that user.

Metadata

Identifiers ending in ItemMetadata are loaded and then set as metadata on the associated item that contains the specified file. Conceptually, this is the same as calling the PUT item/{id}/metadata endpoint.

Annotations

Identifiers ending in AnnotationFile are loaded as annotations, associated with the item that contains the specified file. Conceptually, this is the same as uploaded the file via the annotation endpoints for the item associated with the specified fileId or itemId.

If the annotation file contains any annotations with elements that contain girderId values, the girderId values can be identifier values from files that were uploaded with a reference record that contains a matching uuid field. The uuid field is required for this, but is treated as an arbitrary string.

Funding

This work was funded in part by the NIH grant U24-CA194362-01.

Project details


Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

histomicsui-1.4.5.dev10.tar.gz (705.4 kB view details)

Uploaded Source

Built Distribution

histomicsui-1.4.5.dev10-py2.py3-none-any.whl (209.2 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file histomicsui-1.4.5.dev10.tar.gz.

File metadata

  • Download URL: histomicsui-1.4.5.dev10.tar.gz
  • Upload date:
  • Size: 705.4 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.17

File hashes

Hashes for histomicsui-1.4.5.dev10.tar.gz
Algorithm Hash digest
SHA256 a44fc8e88f5f0b621b9680c2c49400b293d2d274872a368245311d1f625f673e
MD5 5b8f08b1f95680924e030fee58faeb12
BLAKE2b-256 2ea3bf9ebd14c8761a112cb06b1f230419b64fc0b3ff79453e4492f267a3c5f3

See more details on using hashes here.

Provenance

File details

Details for the file histomicsui-1.4.5.dev10-py2.py3-none-any.whl.

File metadata

File hashes

Hashes for histomicsui-1.4.5.dev10-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 0c0c29ddfbbac44a86aa9c4b4ffb7dda28b19d6bcc7a761eb7658b21afd79c70
MD5 36b8f27489a1a0991bf466a68191e75d
BLAKE2b-256 9732f9bb1da82117a39c15b6a1f1b474fdab47eb6eca96233c87cf39b394e8fa

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page