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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

A combined learning algorithm for prostate segmentation on 3D CT images.

Ling Ma¹, Rongrong Guo¹, Guoyi Zhang¹

¹Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA, USA.

Medical Physics

|August 24, 2017

Summary

This summary is machine-generated.

A novel learning-based method combines population and patient-specific data for accurate prostate segmentation on CT scans. This approach improves segmentation robustness and accuracy for prostate cancer diagnosis and treatment planning.

Keywords:

computed tomography image segmentation population-based learning prostate

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Area of Science:

Medical Imaging
Computational Anatomy
Machine Learning in Medicine

Background:

Prostate segmentation on CT images is crucial for prostate cancer diagnosis and treatment.
Low soft-tissue contrast in CT images presents a significant challenge for accurate prostate segmentation.

Purpose of the Study:

To propose a learning-based segmentation method for prostate gland on three-dimensional (3D) CT images.
To enhance the robustness and accuracy of prostate segmentation by combining population-based and patient-specific learning.

Main Methods:

A hybrid approach combining population learning and patient-specific learning models was developed.
A population model was trained on group data, while a patient-specific model incorporated user-marked data.
An adaptive thresholding method was used to convert likelihood maps into binary prostate segmentations.

Main Results:

The algorithm was validated on 3D CT volumes from 92 patients.
Manual segmentations by experienced radiologists served as the gold standard.
The proposed method achieved a Dice similarity coefficient of 87.18 ± 2.99% compared to manual segmentation.

Conclusions:

The combined population and patient-specific learning method effectively segments the prostate on 3D CT images.
This segmentation technique shows promise for applications such as prostate volume measurement and treatment planning.