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Introduction:Magnetic Resonance Imaging, or MRI, can include a specialized imaging technique of the urinary system known as Magnetic Resonance Urography (MRU). This radiation-free technique uses strong magnetic fields and radio waves to produce detailed images with the help of a computer. MRU is particularly effective for visualizing fluid-filled structures like the kidneys, ureters, and bladder.Applications of MRI in the Genitourinary SystemKidneys and Ureters: MRI detects tumors, cysts,...
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Related Experiment Video

Updated: Aug 6, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Prostate cancer segmentation from MRI by a multistream fusion encoder.

Mingjie Jiang1, Baohua Yuan1,2, Weixuan Kou1

  • 1Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China.

Medical Physics
|March 20, 2023
PubMed
Summary

This study introduces a novel multistream fusion encoder for prostate cancer MRI segmentation, improving accuracy for clinically significant lesions. The efficient framework accelerates MRI interpretation for targeted biopsies and therapies.

Keywords:
multistream fusion encoderpatch-based loss functionprostate lesion segmentation

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Oncology

Background:

  • Multiparametric magnetic resonance imaging (mpMRI) enhances prostate cancer detection over conventional biopsy.
  • Accurate lesion segmentation in mpMRI is crucial for planning MRI-targeted biopsies.
  • Integrating T2-weighted and diffusion-weighted image features presents a challenge for prostate lesion segmentation.

Purpose of the Study:

  • To develop a flexible and efficient multistream fusion encoder for improved mpMRI prostate lesion segmentation.
  • To enhance segmentation accuracy, particularly for small lesions, using a novel patch-based loss function.

Main Methods:

  • A multistream encoder fuses features from T2-weighted and diffusion-weighted images at each network layer.
  • Spatial attention mechanisms dynamically weight image modalities for optimal feature fusion.
  • A patch-based loss function averages local Dice Similarity Coefficients (DSC) for balanced sensitivity to lesions of all sizes.

Main Results:

  • The framework achieved high performance, with F1 scores of 82.2% (lesion-level) and 87.6% (patient-level) on a dataset of 931 images.
  • It outperformed single-stream and existing multistream segmentation networks.
  • Average inference time was rapid at 11.8 ms per axial image.

Conclusions:

  • The proposed framework significantly improves the accuracy and efficiency of prostate lesion segmentation from mpMRI.
  • This advancement can accelerate MRI interpretation workflows for MRI-targeted biopsies and focal therapies.