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Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

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Enhanced Deep Neural Network for Prostate Segmentation in Micro-Ultrasound Images.

Ahmed Al-Qurri¹, Asem Thaher², Mohamed Khaled Almekkawy¹

¹The School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, PA 16802, USA.

Sensors (Basel, Switzerland)

|November 27, 2025

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This study introduces a novel deep learning model for precise prostate segmentation in micro-ultrasound images, improving automated diagnosis for prostate cancer detection and aiding early intervention.

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Prostate cancer diagnosis relies on accurate segmentation, which is challenging manually.
Micro-Ultrasound (US) offers high resolution for prostate imaging, comparable to MRI.
Deep learning advances necessitate precise segmentation for automated prostate cancer diagnosis.

Purpose of the Study:

To develop a precise segmentation model for micro-ultrasound prostate images.
To overcome challenges of indistinct boundaries in micro-US prostate segmentation.
To enhance automated diagnosis and biopsy guidance for prostate cancer.

Main Methods:

A dual-encoder architecture integrating CNN and Transformer encoders.
A Mamba v2-based decoder for enhanced segmentation accuracy.

Keywords:

CNN Hypergraph Neural Network Mamba Micro-Ultrasound Transformer UNet UNet++attention medical imaging

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A Hypergraph Neural Network (HGNN) to model complex correlations.

Main Results:

The proposed model achieved superior performance on micro-US datasets.
Achieved a Dice score of 0.9416 and HD95 of 1.93.
Demonstrated comparable results to state-of-the-art segmentation methods.

Conclusions:

The novel model offers precise micro-ultrasound prostate segmentation.
This advancement supports automated diagnosis and improves prostate cancer detection.
The hybrid architecture effectively captures global and local image features.