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Engineering
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基于深度学习的医学图像分割在轨道计算机断层扫描
中的
应用
JoVE Journal
Engineering
This content is Free Access.
JoVE Journal
Engineering
Application of Deep Learning-Based Medical Image Segmentation
via
Orbital Computed Tomography
Please note that all translations are automatically generated.
Click here for the English version.
基于深度学习的医学图像分割在轨道计算机断层扫描
中的
应用
DOI:
10.3791/64500-v
•
04:48 min
•
November 30, 2022
•
Yeon Woong Chung
2
,
Dong Gyun Kang
,
Yong Oh Lee
,
Won-Kyung Cho
5
1
College of Medicine
,
The Catholic University of Korea
,
2
Department of Ophthalmology
,
St. Vincent’s Hospital
,
3
Yeoncheon Public Medical Center
,
4
Department of Industrial and Data Engineering
,
Hongik University
,
5
Department of Ophthalmology
,
Uijeongbu St. Mary’s Hospital
Chapters
00:04
Introduction
00:42
Eyeball, Optic Nerve, and Extraocular Muscle Masking on the Orbital CT Scans
01:42
Pre-Processing: Window Clipping and Cropping the VOIs
02:16
Four Cross-Validations of the Orbital Segmentation Model
02:57
Results: Segmentation Results of the Orbital Structures
04:01
Conclusion
Summary
Automatic Translation
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Automatic Translation
介绍了轨道计算机断层扫描(CT)图像的目标分割协议。解释了使用超分辨率标记轨道结构地面真相的方法,从CT图像中提取感兴趣的体积,以及使用2D顺序U-Net对轨道CT图像进行多标签分割建模的方法。
Tags
Deep Learning
Medical Image Segmentation
Orbital Computed Tomography
Object Segmentation
Eyeball
Optic Nerve
Extraocular Muscles
Superpixels
SmartPencil
SmartFill
AutoCorrection
Sequence U-Net
Dice Score
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