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Three-dimensional segmentation of computed tomography data using Drishti Paint: new tools and developments.

Yuzhi Hu1,2, Ajay Limaye3, Jing Lu4

  • 1Department of Applied Mathematics, Research School of Physics, Australian National University, Canberra, ACT 2601, Australia.

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Summary
This summary is machine-generated.

Drishti v. 2.7, an open-source software, enhances 3D segmentation of computed tomography (CT) data. New tools improve digital reconstruction and 3D modeling for research applications.

Keywords:
3D Freeform PainterDrishti Paintcomputed tomographygradient thresholdthree-dimensional segmentation

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Area of Science:

  • Scientific imaging and visualization
  • Computational biology and medicine
  • Materials science and engineering

Background:

  • Computed tomography (CT) is crucial for non-destructive internal structure analysis in science, medicine, and industry.
  • Three-dimensional (3D) segmentation of CT data reveals intricate internal features of objects.
  • While commercial software is established, the potential of open-source tools for advanced CT data analysis remains underexplored.

Purpose of the Study:

  • To introduce Drishti v. 2.7, an updated open-source software for volume exploration, rendering, and 3D segmentation.
  • To present novel tools and workflows for enhanced 3D segmentation of CT data.
  • To demonstrate the improved accuracy and precision in digital reconstruction, 3D modeling, and 3D printing using Drishti v. 2.7.

Main Methods:

  • Development and integration of a new gradient thresholding tool for volume data.
  • Implementation of a 3D segmentation protocol utilizing the 3D Freeform Painter tool.
  • Application of the software and protocol to CT scan data of a fossil fish for validation.

Main Results:

  • The new gradient thresholding tool and 3D Freeform Painter protocol enable more precise volume data segmentation.
  • Enhanced digital reconstruction and 3D modeling capabilities were achieved.
  • The workflow demonstrated successful application in a case study involving fossil fish CT data.

Conclusions:

  • Drishti v. 2.7 offers powerful, accessible open-source tools for advanced 3D segmentation of CT data.
  • The new tools and workflow significantly improve the accuracy of digital reconstruction and 3D modeling.
  • The methodology is broadly applicable across biological, medical, and industrial research fields requiring detailed internal structure analysis.