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Related Experiment Video

Updated: Sep 20, 2025

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound

Published on: March 21, 2025

Light Weighted Healthcare CNN Model to Detect Prostate Cancer on Multiparametric MRI.

Mukesh Soni¹, Ihtiram Raza Khan², K Suresh Babu³

¹Senior IEEE Member, Bhopal, India.

Computational Intelligence and Neuroscience

|June 7, 2022

Summary

This summary is machine-generated.

The SEMRCNN model accurately identifies prostate cancer in multiparametric MRI scans. This deep learning approach enhances lesion segmentation and outperforms existing methods.

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Last Updated: Sep 20, 2025

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MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent

MR Molecular Imaging of Prostate Cancer with a Small Molecular CLT1 Peptide Targeted Contrast Agent

Published on: September 3, 2013

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Multiparametric magnetic resonance imaging (MP-MRI) is crucial for prostate cancer detection.
Accurate segmentation of prostate cancer lesions in MP-MRI is challenging.
Deep learning models offer potential for automated segmentation.

Purpose of the Study:

To propose and evaluate the SEMRCNN model for autonomous prostate cancer lesion segmentation in MP-MRI.
To investigate the efficacy of feature map exploration and fusion for improved segmentation.
To compare SEMRCNN performance against other segmentation models.

Main Methods:

Developed the SEMRCNN model integrating two parallel convolutional networks for apparent diffusion coefficient (ADC) and T2W images.
Utilized extrusion and excitation blocks to enhance feature fusion.
Trained and validated the model on 140 MP-MRI instances.

Main Results:

SEMRCNN achieved a Dice coefficient of 0.654, sensitivity of 0.695, specificity of 0.970, and positive predictive value of 0.685.
Demonstrated superior performance compared to V net, Resnet50-U-net, Mask-RCNN, and U-net models.
The model effectively segmented lesions by identifying locations and improving feature learning.

Conclusions:

SEMRCNN provides accurate and automated segmentation of prostate cancer in MP-MRI.
The proposed feature fusion strategy enhances segmentation precision.
SEMRCNN represents a significant advancement in AI-driven prostate cancer diagnostics.