Spontaneous breaking of symmetry in overlapping cell instance segmentation using diffusion models.

Area of Science:

• Computer Vision
• Biomedical Imaging
• Machine Learning

Background:

• Instance segmentation assigns unique IDs to objects, a challenge due to semantic symmetry.
• Current deep learning methods struggle with overlapping labels common in biomedical imaging, like cell layers.
• Existing approaches often fail when dealing with complex, overlapping structures.

Purpose of the Study:

• To address the limitations of current instance segmentation methods in biomedical imaging.
• To develop a novel approach for instance segmentation that can handle overlapping labels.
• To introduce a diffusion model-based method that spontaneously breaks symmetry for improved segmentation.

Main Methods:

• A novel instance segmentation approach based on diffusion models was developed.
• The method is designed to spontaneously break symmetry between semantically similar objects.
• Pixel-level instance segmentations are generated, accommodating overlapping labels.

Main Results:

• The proposed diffusion model-based method achieves performance comparable to established models like Cellpose.
• The approach successfully handles overlapping labels, a significant challenge in biomedical image analysis.
• Demonstrated effectiveness on the Cellpose fluorescent cell dataset.

Conclusions:

• Diffusion models offer a promising solution for instance segmentation, particularly in scenarios with overlapping labels.
• The novel method provides a robust alternative for biomedical image analysis tasks.
• This work advances the capability of instance segmentation for complex biological structures.