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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
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Polymorph segmentation representation for medical image computing.

Csaba Pinter1, Andras Lasso1, Gabor Fichtinger1

  • 1Laboratory for Percutaneous Surgery, School of Computing, 557 Goodwin Hall, Queen's University, K7L 2N8, Kingston, Ontario, Canada.

Computer Methods and Programs in Biomedicine
|March 24, 2019
PubMed
Summary
This summary is machine-generated.

A new software library simplifies managing medical image segmentation data across multiple formats. This tool ensures data consistency and provenance, enhancing usability for both users and developers in medical image computing.

Keywords:
3D SlicerDICOMOpen-sourceSegmentationSoftware libraryVoxelization

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Area of Science:

  • Medical image computing
  • Scientific software development
  • Data management

Background:

  • Medical image segmentation often requires managing data in various formats like labelmaps and surface models.
  • Challenges include selecting conversion methods, maintaining data provenance, ensuring consistency, and managing in-memory object coherence.

Purpose of the Study:

  • To develop a software library for automatic management of multiple data representations in medical image segmentation.
  • To address challenges in data conversion, provenance, consistency, and coherence.

Main Methods:

  • A complex data container preserves identity and provenance, ensuring coherence.
  • On-demand conversions are managed by a graph of implemented algorithms, ensuring consistency.
  • A core library (PolySeg in VTK) and a high-level application layer (in 3D Slicer) were developed.

Main Results:

  • Core conversion algorithms were validated and found accurate and reliable.
  • The library facilitated rapid prototyping of clinical applications in areas like gel dosimetry, beam planning, and image fusion.
  • Applications demonstrated improved usability and ease of software development.

Conclusions:

  • A generic, open-source software library simplifies multi-format data management for segmentation tasks.
  • The library enhances user experience with convenient workflows and developer experience with robust prototyping.
  • The software promotes wider adoption and development in medical image analysis.