Topology-aware segmentation for tubular structure in 3D microscopy.

Area of Science:

• Biomedical Imaging
• Computational Biology
• Neuroscience

Background:

• High-resolution 3D microscopy is vital for analyzing complex biological structures like vascular and neural networks.
• Segmenting these tubular structures is challenging due to their intricate branching, varying diameters, and dense packing.
• Existing deep learning methods struggle with preserving global connectivity and local morphological consistency.

Purpose of the Study:

• To develop a 3D topology-supervised segmentation framework for improved tubular structure reconstruction.
• To enhance connectivity preservation and morphological accuracy in segmented microvascular and neurite networks.
• To overcome limitations of current methods in handling complex tubular architectures.

Main Methods:

• A fully 3D topology-supervised segmentation framework incorporating radius-aware topology constraints.
• Adaptive topology-error modulation to focus supervision on critical segmentation errors.
• Large-receptive-field convolutions for capturing long-range directional continuity and thin terminal branches.

Main Results:

• The framework successfully preserves global 3D connectivity and physiological radius profiles.
• Achieved state-of-the-art Dice/clDice scores on diverse datasets (SELMA3D, Mini-vessel, FISBe).
• Demonstrated consistent performance across electron microscopy, optical microscopy, and fluorescence imaging modalities.

Conclusions:

• The proposed method significantly improves segmentation accuracy and morphological consistency for tubular structures in 3D microscopy.
• It offers a robust solution for analyzing complex vascular and neural networks across various imaging conditions.
• This advancement facilitates more precise quantitative analysis in biomedical research and disease studies.