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AnatomySketch: An Extensible Open-Source Software Platform for Medical Image Analysis Algorithm Development.

Mingrui Zhuang1, Zhonghua Chen1,2, Hongkai Wang3,4

  • 1School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, 116024, China.

Journal of Digital Imaging
|June 29, 2022
PubMed
Summary

AnatomySketch is an open-source software platform that accelerates medical image analysis (MIA) algorithm development. It provides a user-friendly GUI and plugin interface for tasks like data annotation and GUI construction, bridging the gap between lab prototypes and clinical validation.

Keywords:
Algorithm developmentDeep learningImage annotationMedical image analysisUser interaction

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Area of Science:

  • Medical Image Analysis
  • Software Engineering
  • Artificial Intelligence

Background:

  • Medical image analysis (MIA) algorithm development is complex, involving model training, data visualization, and GUI construction.
  • Existing tools often lack comprehensive support for all development stages, particularly for deep learning (DL) algorithms requiring data annotation and GUI creation.
  • Accelerating the development cycle from laboratory prototyping to clinical validation is crucial for advancing MIA.

Purpose of the Study:

  • To introduce AnatomySketch, an extensible open-source software platform designed to streamline the development of MIA algorithms.
  • To provide algorithm developers with a unified tool that supports multiple development sub-steps, including data annotation and GUI construction.
  • To facilitate the integration of user-developed algorithm modules and enable rapid GUI-based software prototyping for clinical validation.

Main Methods:

  • Developed AnatomySketch, an open-source platform featuring a user-friendly GUI and a flexible plugin interface.
  • Integrated stylus and multi-touch screen support for efficient image annotation.
  • Incorporated tools to foster collaboration between human experts and artificial intelligence (AI) algorithms.
  • Demonstrated functionality through four exemplar applications: MRI diagnosis, lung lobe segmentation, spine disc segmentation, and Annotation-by-iterative-Deep-Learning (AID).

Main Results:

  • AnatomySketch successfully accelerates the development of MIA algorithms by providing a cohesive platform for various development stages.
  • The software facilitates rapid prototyping of GUI-based applications for clinical validation through its plugin architecture.
  • Exemplar applications showcased the platform's utility in diverse tasks, including DL model training and human-AI collaborative segmentation.
  • The open-source nature of AnatomySketch promotes accessibility and further development within the research community.

Conclusions:

  • AnatomySketch effectively bridges the gap between laboratory prototyping and clinical testing for MIA algorithms.
  • The platform enhances the efficiency of developing and validating complex algorithms, particularly those involving deep learning.
  • AnatomySketch's extensible design and collaborative features support the advancement of medical image analysis research and application.