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CylinGCN: Cylindrical structures segmentation in 3D biomedical optical imaging by a contour-based graph convolutional

Zhichao Liang¹, Shuangyang Zhang¹, Anqi Wei¹

¹School of Biomedical Engineering, Southern Medical University, Guangzhou, 510515, China; Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, 510515, China; Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, 510515, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

|December 1, 2023

Summary

This study introduces CylinGCN, a novel graph neural network for segmenting 3D cylindrical structures in biomedical images. CylinGCN ensures continuous segmentation by integrating geometric features and topological relationships, achieving state-of-the-art results.

Keywords:

3D biomedical optical imaging Cylindrical structures segmentation Graph convolutional network

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Area of Science:

Biomedical Optical Imaging
Medical Image Analysis
Computational Anatomy

Background:

Cylindrical structures like blood vessels and airways are common in biomedical imaging.
Accurate segmentation of these structures is crucial for physiological analysis.
Existing methods struggle with geometric continuity and topological features.

Purpose of the Study:

To develop a novel deep learning method for accurate 3D segmentation of cylindrical structures in biomedical optical imaging.
To address limitations of traditional and classification-based deep learning methods in preserving geometric continuity.

Main Methods:

Introduced CylinGCN, a contour-based graph neural network treating cylindrical structures as 3D graphs.
Employed a multiscale 3D semantic feature extractor and a residual graph convolutional network (GCN) contour generator.
Integrated semantic features with cylindrical topological priors for segmentation.

Main Results:

CylinGCN achieved state-of-the-art performance in segmenting 3D cylindrical structures.
Demonstrated effectiveness on photoacoustic tomography (PAT) and optical coherence tomography (OCT) data.
Ensured continuous and geometrically accurate segmentation surfaces.

Conclusions:

CylinGCN offers a robust and effective solution for 3D cylindrical structure segmentation in biomedical optical imaging.
The graph neural network approach successfully captures complex topological relationships and geometric features.
The method shows significant potential for advancing tissue physiology analysis.