Efficient, interactive, and three-dimensional segmentation of cell nuclei in thick tissue sections

Area of Science:

• * Biology
• * Image Analysis
• * Microscopy

Background:

• * Accurate segmentation of intact cell nuclei in 3D images is crucial for biological research.
• * Automatic algorithms often fail to segment all nuclei in thick tissue sections, necessitating interactive methods.
• * Current interactive methods are labor-intensive, requiring border delineation in every 2D plane of a 3D image.

Purpose of the Study:

• * To develop a more efficient interactive algorithm for segmenting cell nuclei in 3D images.
• * To reduce the user effort required for accurate nuclear segmentation in thick tissue sections.
• * To improve the speed and accuracy of 3D cell nucleus segmentation for biological studies.

Main Methods:

• * An interactive algorithm that requires users to draw nuclear borders only in central 2D planes where borders are most distinct.
• * The algorithm interpolates the entire nuclear surface from a minimal set of user-defined borders (five per nucleus).
• * An optional automatic surface optimization step to refine the accuracy of the segmented nuclear surface.

Main Results:

• * The algorithm successfully segments objects corresponding to individual, visually identifiable cell nuclei.
• * User interaction is significantly reduced compared to existing methods that require delineation in all 2D planes.
• * The segmented surfaces closely represent the true nuclear surface, with potential for further refinement.

Conclusions:

• * The developed algorithm offers a more efficient and user-friendly approach to 3D cell nucleus segmentation.
• * This method enhances the feasibility of detailed nuclear analysis in complex biological samples.
• * The algorithm provides a valuable tool for researchers requiring accurate segmentation of cell nuclei in 3D imaging studies.