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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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FGCN: Image-Fused Point Cloud Semantic Segmentation with Fusion Graph Convolutional Network.

Kun Zhang¹, Rui Chen¹, Zidong Peng²

¹College of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China.

Sensors (Basel, Switzerland)

|October 14, 2023

Summary

This study introduces a Fusion Graph Convolutional Network (FGCN) for enhanced semantic segmentation using multi-modal data. The FGCN improves accuracy in scene interpretation for applications like autonomous driving.

Keywords:

FGCN graph attention convolution multi-modal data multi-scale features point clouds

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Area of Science:

Computer Vision
Machine Learning
Robotics

Background:

Semantic segmentation is vital for scene understanding in autonomous systems.
Multi-modal data fusion enhances feature extraction compared to single-modal approaches.
Existing methods struggle with efficient and accurate feature extraction from combined image and point cloud data.

Purpose of the Study:

To propose a novel Fusion Graph Convolutional Network (FGCN) for multi-modal semantic segmentation.
To improve the accuracy and efficiency of point cloud semantic segmentation by integrating image data.
To enhance the generalization capability and feature distinction of segmentation networks.

Main Methods:

Developed a Fusion Graph Convolutional Network (FGCN) integrating image and point cloud data.
Implemented a two-channel k-nearest neighbors (KNN) module for efficient feature extraction from image data.
Utilized a spatial attention mechanism and multi-scale feature fusion within the FGCN.

Main Results:

Achieved a mean intersection over union (MIoU) of 88.06% on the self-made SSKIT dataset.
Reached an MIoU of 78.55% on the public S3DIS dataset, outperforming existing methods.
Demonstrated significant improvements in segmentation accuracy and data feature enhancement.

Conclusions:

The proposed FGCN effectively fuses multi-modal data for improved semantic segmentation.
The method enhances feature representation and achieves state-of-the-art performance on benchmark datasets.
Validated the effectiveness of the FGCN for applications requiring accurate scene interpretation.