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Related Experiment Video

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DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation.

Qifei Dong1, Gang Luo1, David Haynor2

  • 1Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, 98195, USA.

Journal of Digital Imaging
|July 16, 2020
PubMed
Summary
This summary is machine-generated.

DicomAnnotator is a new open-source software that efficiently annotates medical images, addressing the need for faster data preparation in machine learning. This tool supports various annotation types and DICOM formats, improving medical image analysis workflows.

Keywords:
DICOMImage annotationMachine learningOpen sourceSoftware design

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Area of Science:

  • Medical Imaging
  • Machine Learning
  • Software Development

Background:

  • Supervised machine learning for medical image analysis requires extensive annotated datasets.
  • Manual image annotation is time-consuming and labor-intensive, hindering research progress.

Purpose of the Study:

  • To develop a configurable, open-source software program for efficient annotation of Digital Imaging and Communications in Medicine (DICOM) images.
  • To address the limitations of existing software in supporting diverse annotation tasks and DICOM compatibility.

Main Methods:

  • Designed and implemented DicomAnnotator, an open-source software program.
  • Configured the software for various annotation tasks, including efficient placement of multiple annotation types.
  • Enabled annotator attribution and DICOM image display capabilities.

Main Results:

  • DicomAnnotator successfully fulfills the requirements for configurable, efficient DICOM image annotation.
  • Evaluation using spine image annotation demonstrated efficient use by annotators with diverse backgrounds.
  • The software provides user-friendly features to streamline the annotation process.

Conclusions:

  • DicomAnnotator effectively fills the gap for a versatile open-source DICOM image annotation tool.
  • The software can significantly expedite the creation of annotated datasets for machine learning in medical imaging.
  • Freely available under GPLv3 license, promoting wider adoption in research.