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Updated: Feb 15, 2026
09:11
Use of MRI-ultrasound Fusion to Achieve Targeted Prostate Biopsy
Published on: April 9, 2019
22.5K
PSNet: prostate segmentation on MRI based on a convolutional neural network.
Zhiqiang Tian1,2, Lizhi Liu2, Zhenfeng Zhang3
1Xi'an Jiaotong University, School of Software Engineering, Xi'an, China.
Journal of Medical Imaging (Bellingham, Wash.)
|January 30, 2018
Summary
We developed a deep convolutional neural network (CNN) for automatic prostate segmentation on MRI scans. This AI model accurately segments the prostate, aiding in cancer diagnosis and treatment planning.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Oncology
Background:
- Accurate prostate segmentation on MRI is crucial for cancer diagnosis and therapy.
- Manual segmentation is time-consuming and prone to inter-observer variability.
Purpose of the Study:
- To develop and evaluate a deep fully convolutional neural network (CNN) for automated prostate segmentation on MRI.
- To improve the efficiency and accuracy of prostate segmentation in clinical workflows.
Main Methods:
- A deep fully convolutional neural network (CNN), termed PSNet, was proposed for end-to-end training.
- The model was trained using prostate MRI scans and corresponding ground truth segmentations.
- Experiments were conducted on three datasets comprising 140 patient MRI scans.
Main Results:
- The PSNet model achieved a mean Dice similarity coefficient of [Formula: see text] compared to manual segmentations.
- The proposed CNN model demonstrated satisfactory performance in segmenting the prostate on MRI.
- The automated segmentation process was efficient and reproducible.
Conclusions:
- The developed deep CNN (PSNet) offers a reliable and automated solution for prostate segmentation on MRI.
- This approach has the potential to enhance prostate cancer diagnosis and therapy planning.
- Further validation on larger and diverse datasets is warranted to confirm clinical utility.
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