×
模态框(Modal)标题
在这里添加一些文本
Close
Close
Submit
Cancel
Confirm
×
模态框(Modal)标题
×
Welcome to visit Fudan University Journal of Medical Sciences,
Share:
Toggle navigation
Home
About
About Journal
Honor
Indexed In
Editorial Board
Editorial Board
Youth Editorial Board
Instruction
FAQ
Journal Online
Just Accepted
Current Issue
Archive
Most Read
Most Download
Most Cited
E-mail Alert
Download
Journal Policy
Academic Publishing Standards
Ethical Policies
Copyright Policy
Review Process
Correction and Withdrawal
Others
Contact Us
中文
Figure/Table detail
Feasibility study on automatic segmentation of pelvic intestinal tube for radiotherapy images based on deep learning
Yu-jie ZHANG, Xu YUAN, Han XIAO, Jian-ying ZHANG
Fudan University Journal of Medical Sciences
, 2024, 51(
02
): 243-248. DOI:
10.3969/j.issn.1672-8467.2024.02.015
Geometric index
Original model
Rec60
t
P
DSC
0.65±0.13
0.81±0.08
-8.013
<
0.001
HD95
29.91±20.46
17.51±15.91
5.962
<
0.001
ASSD
8.96±10.23
3.82±4.10
3.889
<
0.001
Tab 2
DSC, HD95 and ASD for the original model and Rec60 model in the test cases
(${\bar x}$±
s
,
n
=40)
Other figure/table from this article
Fig 1
Manual segmentation (red), original automatic segmentation (blue) and trained automatic segmentation (green) of the small intestine in 3 cases
Tab 1
Number of cases used in the 4 models
Fig 2
Data distribution and box plots for DSC, HD95 and ASD in the test cases (
n
=40)
Paired
t
test between Rec60 and the original model.
Tab 3
The mean DSCs of the 5 automatic segmentation models and their paired
t
tests
Tab 4
The mean HD95s of the 5 automatic segmentation models and their paired
t
tests
Tab 5
The mean ASSDs of the 5 automatic segmentation models and their paired
t
test