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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Deep learning application engine (DLAE): Development and integration of deep learning algorithms in medical imaging.

Jeremiah W Sanders1,2, Justin R Fletcher3, Steven J Frank4

  • 1Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, 1515 Holcombe Blvd., Unit 1472, Houston, TX 77030, United States of America.

Softwarex
|June 11, 2021
PubMed
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A new deep learning (DL) application engine (DLAE) offers a code-free solution for medical imaging. This open-source system facilitates the integration of DL models into clinical workflows, enhancing diagnostic capabilities.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Clinical Informatics

Background:

  • Deep learning (DL) offers significant potential in medical imaging analysis.
  • Integrating DL tools into existing clinical workflows can be complex and requires specialized expertise.
  • A need exists for user-friendly, accessible DL solutions for medical professionals.

Purpose of the Study:

  • To introduce the concept of a Deep Learning Application Engine (DLAE).
  • To present potential applications and integration pathways for DLAE in clinical settings.
  • To demonstrate the development and utility of an open-source, code-free DL software for medical imaging.

Main Methods:

  • Development of an open-source software application, DLAE, enabling code-free DL.
Keywords:
Algorithm developmentDeep learningMedical imagingSoftware

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  • Implementation of support for various DL techniques: CNNs, FCNs, GANs, and bounding box detectors.
  • Testing DLAE with example applications using clinical images and demonstrating model deployment in commercial software.

    • Main Results:

    • DLAE provides a code-free approach to applying DL in medical imaging.
    • The system supports diverse DL architectures relevant to medical image analysis.
    • Successful integration of trained DL models into a clinical software package was demonstrated.

    Conclusions:

    • DLAE simplifies the adoption of deep learning in medical imaging.
    • The open-source nature and code-free interface of DLAE promote wider accessibility.
    • DLAE facilitates the seamless integration of advanced AI tools into clinical practice.