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Ultrasound Prostate Segmentation Using Adaptive Selection Principal Curve and Smooth Mathematical Model.

Tao Peng^1,2,3, Yiyun Wu⁴, Jing Zhao⁵

¹School of Future Science and Engineering, Soochow University, Suzhou, China. sdpengtao401@gmail.com.

Journal of Digital Imaging

|February 2, 2023

Summary

This study introduces a new hybrid method for precise prostate segmentation in ultrasound images, overcoming common challenges like low contrast and unclear boundaries. The developed technique achieves superior accuracy and robustness for clinical applications.

Keywords:

Fractional-order-based neural network Mean shift clustering Principal curve Smooth mathematical model Ultrasound prostate segmentation

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Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Biomedical Engineering

Background:

Accurate prostate segmentation in ultrasound images is vital for diagnosing prostate cancer and guiding surgery.
Ultrasound images present segmentation challenges due to low signal-to-noise ratio, poor contrast, and indistinct prostate boundaries.

Purpose of the Study:

To develop a novel hybrid method for accurate prostate segmentation in ultrasound images.
To generate precise prostate contours from diverse ultrasound datasets.

Main Methods:

A hybrid approach combining a principal curve-based method with a fractional-order neural network.
The method utilizes projection indices from principal curves as input for the neural network.
A smooth mathematical map is generated by the neural network to represent the prostate boundary.

Main Results:

The proposed hybrid method demonstrated superior performance compared to state-of-the-art segmentation techniques.
The method achieved accurate prostate segmentation across multiple datasets from various institutions.
Experimental evaluations confirmed the robustness of the method across different evaluation metrics.

Conclusions:

The novel hybrid method offers a robust and accurate solution for prostate segmentation in ultrasound imaging.
This technique has significant potential for improving prostate cancer diagnosis and image-guided surgical procedures.