Nucleus segmentation: towards automated solutions.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Cell Biology

Background:

• Single nucleus segmentation is a critical, yet challenging, first step in microscopy image analysis.
• Accurate segmentation directly impacts downstream analyses like cell tracking, feature extraction, and phenotype classification.
• Despite advancements, a universally accepted solution and benchmarking platform for cell segmentation remain elusive.

Purpose of the Study:

• To review and present the latest single-cell segmentation tools.
• To provide an interactive method browser for selecting appropriate segmentation solutions.
• To address the ongoing challenge of accurate cell segmentation in microscopy.

Main Methods:

• Comprehensive literature review of recent single-cell segmentation techniques.
• Evaluation of existing segmentation tools and strategies.
• Development of an interactive browser for method selection.

Main Results:

• Significant improvements in single-cell segmentation accuracy have been achieved through large annotated datasets and deep learning.
• Existing 2D segmentation strategies have been successfully extended to 3D applications.
• No single, universally accepted segmentation solution or benchmarking platform currently exists.

Conclusions:

• The field of single-cell segmentation has seen rapid progress, with deep learning showing increased accuracy.
• An interactive tool is provided to guide researchers in selecting optimal segmentation methods.
• Further development of standardized benchmarking platforms is needed to advance the field.