DICOM Analytics and Archive (DIANA)

Derek Merck derek_merck@brown.edu
Brown University and Rhode Island Hospital
Winter 2018

Source: https://www.github.com/derekmerck/DIANA
Documentation: https://diana.readthedocs.io

Dependencies

• Python 3.6
• Many Python packages
• Many Ansible roles
• Many Docker containers

Overview

Hospital picture archive and communications systems (PACS) are not well suited for "big data" analysis. It is difficult to identify and extract datasets in bulk, and moreover, high resolution data is often not even stored in the clinical systems.

DIANA is a DICOM imaging informatics platform that can be attached to the clinical systems with a very small footprint, and then tuned to support a range of tasks from high-resolution image archival to cohort discovery to radiation dose monitoring. It provides DICOM services, image data indexing, REST endpoints for scripting, and user access control through an amalgamation of free and free and open source (FOSS) systems.

Installation

From pypi:

pip3 install diana_plus

From source:

$ git clone https://www.github.com/derekmerck/DIANA
$ pip install -e DIANA/packages/guidmint DIANA/packages/diana

Organization

diana package

• utils contains generic code with no references to diana-dixels or endpoints.
• apis contains get/put/handle functions for diana-dixels ("DICOM elements") and enpoints including Orthanc, Montage, Splunk, and files on disk.
• daemon contains higher level tasks that compose multiple apis, like file and PACS monitoring, building secondary registries, and automatic deidentification and routing
• star overloads apis with a celery-friendly wrapper function (something like do-star(object, func, item))

guidmint package

• guidmint implements "mints" for flexible, reproducible anonymization, hashing schemes, and generating repeatable global uids and sham names/dobs

Can also be installed independently with pip3 install guidmint

halibut package

• halibut utility code for running Keras MobileNet classifiers on DICOM images (contributed by Ian Pan), additional requirements: scipy and keras

apps

• cli contains command-line interface wrappers for common diana functions such as querying endpoints and saving images

• diana-worker creates a diana+ celery worker ("diana*")

• get-a-guid is a REST API for guidmint

• radcatr is a simple TKL UI for basic report review and annotation

• study-manager is a simple, dynanmically configured DIANA front-end web portal for uploading and accessing available imaging resources from multiple trials and studiers. A reference implementation is at http://www.central-imaging.com/.

• splunk-apps are apps and dashboards for informatics and data review:

  • DIANA-status: DIANA services introspection
  • RadRx: DICOM structured dose record monitoring
  • RadFlow: hl7 feed analysis and radiologist workload balancing
  • RadClf: Radiology report NLP classification

tests

• resources includes some simple, anonymized DICOM files are included to test apis for upload, download, caching, etc.
• bench provides a dev configuraqtion for testing with vagrant
• unit collection of short function verfications

See tests

The DIANA Service Stack

A simple DIANA stack requires two basic services:

• An Orthanc DICOM node for storing, pulling, proxying DICOM data
• A Splunk database for indexing available data

Additional services can be added:

• File handlers for reading/writing DCM, png, text, and csv files
• Persistent (Redis, csv) or in-memory caches for worklist
• Report handlers for extracting and anonymizing report data
• AI handlers for image analysis

A set of distributed "star" apis shadow the vanilla api names for building workflows with the celery async manager. In this case, two additional services are required:

• A Redis messenger
• One or more "diana-workers" attached to various queues depending on their hardware (file or report access, machine learning hardware, proxying ip)

A basic stack can be configured with Ansible using Vagrant and the testbench_playbook.yml inventory.

Ansible Roles

The cirr_playbook.yml is used with a private inventory to setup the Lifespan CIRR. The central_im_playbook.yml is used to configure the Central Imaging archive.

Several roles for containerized services are available on Ansible-Galaxy for these playbooks.

• derekmerck.diana-docker
• derekmerck.nginx-docker
• derekmerck.orthanc-docker
• derekmerck.pureftpd-docker
• derekmerck.redis-docker
• derekmerck.snappass-docker
• derekmerck.splunk-docker

Docker Images

reDiana is a docker-compose file for setting up a Remote Embedded DIANA instance on a single-board computer, such as a Raspberry Pi. It is particularly designed to be controlled through the Resin.io IoT platform.

Several multi-architecture Docker images are available on Docker Hub for these roles and compositions.

• derekmerck/orthanc
• derekmerck/conda and derekmerck/keras-tf
• derekmerck/diana and derekmerck/diana-learn (includes Halibut)

These containers are built for both amd64 and arm32v7 architectures on travis-ci as part of testing, so they are always available from docker hub.

Future Work

muDIANA

(Planned) Extensions supporting high-throughput 3D microscopy data and image analytics and archive

• Monitoring for microscopy use logs
• Post-processing including ROI cropping and 3D CLAHE

Verifying Files

md5 hashes for the apps and packages directories are published on the DIANA Hashes Gist as diana_hashes.json.

A simple hash verification utility will compare local file hashes to the public hashes:

pip install git+https://github.com/derekmerck/check-hashes
python3 check-hashes.py verify 4b0bfbca0a415655d97f36489629e1cc diana_hashes apps/cli

License

MIT