Development and validation of fully automated robust deep learning models for multi-organ segmentation from whole-body CT images
Physica Medica,
Год журнала:
2025,
Номер
130, С. 104911 - 104911
Опубликована: Фев. 1, 2025
This
study
aimed
to
develop
a
deep-learning
framework
generate
multi-organ
masks
from
CT
images
in
adult
and
pediatric
patients.
A
dataset
consisting
of
4082
ground-truth
manual
segmentation
various
databases,
including
300
cases,
were
collected.
In
strategy#1,
the
provided
by
public
databases
split
into
training
(90%)
testing
(10%
each
database
named
subset
#1)
cohort.
The
set
was
used
train
multiple
nnU-Net
networks
five-fold
cross-validation
(CV)
for
26
separate
organs.
next
step,
trained
models
strategy
#1
missing
organs
entire
dataset.
generated
data
then
model
CV
(strategy#2).
Models'
performance
evaluated
terms
Dice
coefficient
(DSC)
other
well-established
image
metrics.
lowest
DSC
strategy#1
0.804
±
0.094
adrenal
glands
while
average
>
0.90
achieved
17/26
strategy#2
(0.833
0.177)
obtained
pancreas,
whereas
13/19
For
all
mutual
included
#2,
our
outperformed
TotalSegmentator
both
strategies.
addition,
on
#3.
Our
with
significant
variability
different
producing
acceptable
results
making
it
well-suited
implementation
clinical
setting.
Язык: Английский
A qualitative, quantitative and dosimetric evaluation of a machine learning-based automatic segmentation method in treatment planning for gastric cancer
Physica Medica,
Год журнала:
2025,
Номер
130, С. 104896 - 104896
Опубликована: Янв. 7, 2025
Язык: Английский
Performance of Commercial Deep Learning-Based Auto-Segmentation Software for Prostate Cancer Radiation Therapy Planning: A Systematic Review
Information,
Год журнала:
2025,
Номер
16(3), С. 215 - 215
Опубликована: Март 11, 2025
As
yet,
there
is
no
systematic
review
focusing
on
benefits
and
issues
of
commercial
deep
learning-based
auto-segmentation
(DLAS)
software
for
prostate
cancer
(PCa)
radiation
therapy
(RT)
planning
despite
that
NRG
Oncology
has
underscored
such
necessity.
This
article’s
purpose
to
systematically
DLAS
product
performances
PCa
RT
their
associated
evaluation
methodology.
A
literature
search
was
performed
with
the
use
electronic
databases
7
November
2024.
Thirty-two
articles
were
included
as
per
selection
criteria.
They
evaluated
12
products
(Carina
Medical
LLC
INTContour
(Lexington,
KY,
USA),
Elekta
AB
ADMIRE
(Stockholm,
Sweden),
Limbus
AI
Inc.
Contour
(Regina,
SK,
Canada),
Manteia
Technologies
Co.
AccuContour
(Jian
Sheng,
China),
MIM
Software
ProtégéAI
(Cleveland,
OH,
Mirada
Ltd.
DLCExpert
(Oxford,
UK),
MVision.ai
Contour+
(Helsinki,
Finland),
Radformation
AutoContour
(New
York,
NY,
RaySearch
Laboratories
RayStation
Siemens
Healthineers
AG
AI-Rad
Companion
Organs
RT,
syngo.via
Image
Suite
DirectORGANS
(Erlangen,
Germany),
Therapanacea
Annotate
(Paris,
France),
Varian
Systems,
Ethos
(Palo
Alto,
CA,
USA)).
Their
results
illustrate
can
delineate
organs
at
risk
(abdominopelvic
cavity,
anal
canal,
bladder,
body,
cauda
equina,
left
(L)
right
(R)
femurs,
L
R
pelvis,
proximal
sacrum)
four
clinical
target
volumes
(prostate,
lymph
nodes,
bed,
seminal
vesicle
bed)
clinically
acceptable
outcomes,
resulting
in
delineation
time
reduction,
5.7–81.1%.
Although
recommended
each
centre
perform
its
own
prior
implementation,
seems
more
important
due
methodological
respective
single
studies,
e.g.,
small
dataset
used,
etc.
Язык: Английский
A bibliometric analysis of artificial intelligence applied to cervical cancer
Frontiers in Medicine,
Год журнала:
2025,
Номер
12
Опубликована: Апрель 8, 2025
This
study
conducts
a
bibliometric
analysis
of
artificial
intelligence
(AI)
applications
in
cervical
cancer
to
provide
comprehensive
overview
the
research
landscape
and
current
advancements.
Relevant
publications
on
AI
were
retrieved
from
Web
Science
Core
Collection.
Bibliometric
was
performed
using
CiteSpace
VOSviewer
assess
publication
trends,
authorship,
country
institutional
contributions,
journal
sources,
keyword
co-occurrence
patterns.
From
1996
2024,
our
770
showed
surge
research,
with
86%
published
last
5
years.
China
(315
pubs,
32%)
US
(155
16%)
top
contributors.
Key
institutions
Chinese
Academy
Sciences,
Southern
Medical
University,
Huazhong
University
Technology.
Research
hotspots
included
disease
prediction,
image
analysis,
machine
learning
cancer.
Schiffman
led
(12)
citations
(207).
had
highest
(3,819).
Top
journals
"Diagnostics,"
"Scientific
Reports,"
"Frontiers
Oncology."
Keywords
like
"machine
learning"
"deep
indicated
trends.
maps
field's
growth,
highlighting
key
contributors
topics.
provides
valuable
insights
into
trends
hotspots,
guiding
future
studies
fostering
collaboration
enhance
Язык: Английский