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The z-transform is a powerful mathematical tool used in the analysis of discrete-time signals and systems. It is a crucial tool in the analysis of discrete-time systems, but its convergence is limited to specific values of the complex variable z. This range of values, known as the Region of Convergence (ROC), is fundamental in determining the behavior and stability of a system or signal. The ROC defines the region in the complex plane where the z-transform converges, which can take various...

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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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Optimization-based interactive segmentation interface for multiregion problems.

John S H Baxter¹, Martin Rajchl², Terry M Peters¹

¹Western University, Robarts Research Institute, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada; Western University, Biomedical Engineering Graduate Program, 1151 Richmond Street N., London, Ontario N6A 5B7, Canada.

Journal of Medical Imaging (Bellingham, Wash.)

|June 24, 2016

Summary

This summary is machine-generated.

This study introduces a generalized hierarchical max-flow method for interactive medical image segmentation. It enables efficient, coupled multiregion segmentation, overcoming limitations of previous general-purpose tools.

Keywords:

ASETS library convex optimization hierarchical max-flow segmentation interactive segmentation optimization-based segmentation

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Area of Science:

Medical Imaging
Computer Vision
Computational Anatomy

Background:

Interactive segmentation combines manual and automated approaches for medical imaging.
Existing tools struggle with simultaneous multiregion segmentation and label coupling.
Hierarchical max-flow methods were previously application-specific and lacked generality.

Purpose of the Study:

To present a generalized hierarchical max-flow framework for interactive segmentation.
To enable efficient, coupled multiregion segmentation in medical imaging.
To provide a general-purpose tool adaptable to various segmentation tasks.

Main Methods:

Developed an interactive segmentation interface utilizing generalized hierarchical max-flow.
Implemented optimization-based multiregion segmentation guided by user-defined seeds.
Generalized the hierarchical structure to be specified at run-time for flexibility.

Main Results:

Demonstrated the generality of the approach through applications in cardiac and neonatal brain segmentation.
Showcased the ability to segment multiple regions with high coupling between labels.
Enabled rapid exploration of different hierarchical structures for segmentation problems.

Conclusions:

The presented interactive segmentation interface offers a general-purpose solution for complex medical imaging tasks.
Generalized hierarchical max-flow effectively addresses limitations in coupled multiregion segmentation.
The tool's adaptability makes it valuable for diverse applications in medical image analysis.