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

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.