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Graph-convolutional-network-based interactive prostate segmentation in MR images.

Zhiqiang Tian¹, Xiaojian Li¹, Yaoyue Zheng¹

¹School of Software Engineering, Xi'an Jiaotong University, Xi'an, 710049, China.

Medical Physics

|June 14, 2020

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This summary is machine-generated.

This study introduces an interactive segmentation method using a graph convolutional network (GCN) for accurate prostate segmentation in MRI scans, improving prostate cancer diagnosis.

Area of Science:

Medical Imaging
Computer Vision
Machine Learning

Background:

Accurate prostate segmentation from MRI is crucial for prostate cancer diagnosis and treatment.
Existing methods require robust and precise segmentation techniques.

Purpose of the Study:

To develop a robust interactive segmentation method for precise prostate segmentation in MR images.
To enhance the accuracy of prostate segmentation using a novel approach.

Main Methods:

An interactive segmentation method employing a graph convolutional network (GCN) to refine automatic segmentations.
Utilized an atrous multiscale convolutional neural network (CNN) encoder for feature learning.
Introduced a contour matching loss to preserve boundary details and train the GCN.

Keywords:

graph convolutional network interactive segmentation prostate MR image

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Main Results:

Achieved high mean Dice similarity coefficients of 93.8% (in-house) and 94.4% (PROMISE12).
The proposed method demonstrated superior performance compared to existing state-of-the-art segmentation techniques.

Conclusions:

The GCN-based interactive method provides accurate prostate segmentation from MR images.
This technique offers significant potential for applications in prostate cancer imaging.