Skip to main content

This is a consolidation of work from NAMIC efforts primarily at the University of Iowa.

Project description

Introduction

In this work, we developed a robust, easily extensible classification framework that extracts key features from well-characterized DICOM header fields for identifying image modality and acquisition plane. Our tool is crucial for eliminating error-prone human interaction and allowing automatization, increasing imaging applications' reliability and efficiency. We used Random Forrest and Decision Tree algorithms to determine the image modality and orientation. We trained on header meta-data of over 49000 scan volumes from multiple studies and achieved over 99% prediction accuracy on image modality and acquisition plane classification.

This project was supported by several funding sources including:

  • UCSF SCOUTS RO1
  • NIH-NINDS R01NS114405 and NINDS R01 NS119896
  • Botimageai.

Citing

Please reference the manuscript:

Michal Brzus, Cavan J. Riley, Joel Bruss, Aaron Boes, Randall Jones, Hans J. Johnson, "DICOM sequence selection for medical imaging applications," Proc. SPIE 12931, Medical Imaging 2024: Imaging Informatics for Healthcare, Research, and Applications, 12931 (2024)

Instructions

Below are useful commands to start using the tool.

Clone git repo

$ git clone https://github.com/BRAINSia/dcm-classifier.git

Navigate to the cloned repo

$ cd <repo path>

Setup virtual environment

$ python3 -m venv <venv_path> && source <venv_path>/bin/activate

Install required packages

$ pip install -r requirements.txt

Development

$ pip install -r requirements_dev.txt

$ pre-commit install $ pre-commit run -a

Run the script!

python3 classify_study.py -m models/rf_classifier.onnx -d <path_to_dicom_session>

Testing

  pytest
# or to fail on warnings
  python3 -Werror::FutureWarning -m pytest

Coverage Analysis

 coverage run --concurrency=multiprocessing --parallel-mode -m pytest tests --junitxml=tests/pytest.xml
 coverage combine
 coverage report --format=text -m |tee tests/pytest-coverage.txt
 coverage xml -o tests/coverage.xml
 coverage xml -o tests/coverage.xml

Authors

  1. Michal Brzus

    github: mbrzus, email: michal-brzus@uiowa.edu

  2. Hans J. Johnson

    github: BRAINSia, email: hans-johnson@uiowa.edu

Project details


Download files

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

Source Distribution

dcm_classifier-0.6.0rc2.tar.gz (2.2 MB view details)

Uploaded Source

Built Distribution

dcm_classifier-0.6.0rc2-py3-none-any.whl (38.9 kB view details)

Uploaded Python 3

File details

Details for the file dcm_classifier-0.6.0rc2.tar.gz.

File metadata

  • Download URL: dcm_classifier-0.6.0rc2.tar.gz
  • Upload date:
  • Size: 2.2 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/5.0.0 CPython/3.11.6

File hashes

Hashes for dcm_classifier-0.6.0rc2.tar.gz
Algorithm Hash digest
SHA256 2bbbf57e07083e0497b107d5bdc1d9d18e0530a7ca2ee7eda3424111a7cc1794
MD5 34bc2a516382675e161683e970a9e93d
BLAKE2b-256 8050227aa4f3775178af2d67f10b45bfea48cb6ff198b5ffa17c916832528672

See more details on using hashes here.

File details

Details for the file dcm_classifier-0.6.0rc2-py3-none-any.whl.

File metadata

File hashes

Hashes for dcm_classifier-0.6.0rc2-py3-none-any.whl
Algorithm Hash digest
SHA256 924c3c62d354b85ff056a92b44165bf686323ce10b7e3cdce1f99531243d7975
MD5 09cec7dbdea9411fb75418e0ae0c1f11
BLAKE2b-256 9b05e6339cbeb70be2543c13b0a691c6d03f1f5251f62dfb8c9aa84acf2580b1

See more details on using hashes here.

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