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

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A Multi-Source Circular Geodesic Voting Model for Image Segmentation.

Shuwang Zhou^1,2, Minglei Shu², Chong Di²

¹College of Computer Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China.

Entropy (Basel, Switzerland)

|January 8, 2025

Summary

This summary is machine-generated.

PolarVoting enhances image segmentation by combining convolutional neural networks (CNNs) with geometric priors. This novel approach improves accuracy and robustness in challenging computer vision and medical imaging tasks.

Keywords:

geodesic model geodesic voting image segmentation multi-source polar representation

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Area of Science:

Computer Vision
Medical Imaging
Artificial Intelligence

Background:

Convolutional Neural Networks (CNNs) excel in feature learning but lack geometric priors and global context.
Variational methods offer geometric priors but require manual initialization and are sensitive to hyperparameters.
Existing methods struggle with accuracy in complex imaging scenarios.

Purpose of the Study:

To develop a novel image segmentation approach, PolarVoting, that integrates CNNs with geometric priors.
To improve segmentation accuracy and robustness, especially in challenging imaging conditions.
To leverage minimal path encoding and a modified circular geometric voting scheme.

Main Methods:

Utilizing the PolarMask model to extract multiple source points for initialization.
Constructing a voting score map using a modified circular geometric voting (CGV) scheme.
Embedding global geometric information within the voting map for accurate segmentation.

Main Results:

The PolarVoting method demonstrated superior performance compared to PolarMask and traditional single-source CGV models.
Achieved enhanced segmentation accuracy and robustness across various datasets.
Successfully delineated object boundaries in images with intensity inhomogeneity, noise, and complex backgrounds.

Conclusions:

PolarVoting effectively integrates neural network representations with geometric priors for advanced image segmentation.
The proposed method offers a significant advancement in addressing limitations of current segmentation techniques.
PolarVoting shows promise for applications in computer vision and medical imaging requiring precise object delineation.