NVIDIA Unveils VISTA-3D NIM Microservice for Advanced CT Scan Analysis

Over
300
million
computed
tomography
(CT)
scans
are
performed
globally
each
year,
with
85
million
in
the
United
States
alone.
Radiologists
are
continuously
seeking
ways
to
expedite
their
workflow
and
produce
precise
reports.
To
address
this
need,
NVIDIA
Research
has
developed
a
new
foundation
model,
VISTA-3D,
which
is
integrated
into
an
optimized
microservice
called
NVIDIA
NIM,
designed
for
scalable
deployment,
according
to

NVIDIA
Technical
Blog.

VISTA-3D
Model

The
VISTA-3D
(Versatile
Imaging
Segmentation
and
Annotation)
model
is
trained
on
over
12,000
volumes,
covering
127
types
of
human
anatomical
structures
and
various
lesions,
including
lung
nodules,
liver
tumors,
and
bone
lesions.
It
offers
accurate
out-of-box
segmentation
and
state-of-the-art,
zero-shot
interactive
segmentation,
making
it
a
versatile
tool
for
medical
imaging.

The
model
features
three
core
workflows:

• 
  Segment
  everything:
  Allows
  comprehensive
  body
  exploration,
  aiding
  in
  understanding
  complex
  diseases
  affecting
  multiple
  organs.
• 
  Segment
  using
  class:
  Provides
  detailed
  views
  based
  on
  specific
  classes,
  essential
  for
  targeted
  disease
  analysis.
• 
  Segment
  point
  prompts:
  Enhances
  segmentation
  precision
  through
  user-directed
  selection,
  accelerating
  the
  creation
  of
  accurate
  ground-truth
  data.

The
architecture
of
VISTA-3D
includes
an
encoder
layer
followed
by
two
parallel
decoder
layers—one
for
automatic
segmentation
and
another
for
point
prompts.
This
structure
ensures
high
accuracy
and
adaptability
across
diverse
anatomical
areas.

VISTA-3D
NIM
Microservice

Hosted
on
the
NVIDIA
API
Catalog,
the
VISTA-3D
NIM
microservice
allows
users
to
test
its
capabilities
with
sample
data.
It
can
segment
over
100
organs
or
specific
classes
of
interest,
providing
views
in
axial,
coronal,
or
sagittal
planes.

Using
NIM
Microservices

Users
can
run
VISTA-3D
on
their
data
by
signing
up
for
a
personal
key
from
NVIDIA,
which
provides
1,000
free
credits
to
try
any
NIM
microservices.
Detailed
instructions
on
generating
an
API
key
and
running
the
model
are
available,
along
with
sample
code
in
various
programming
languages.

For
those
looking
to
run
VISTA-3D
on
their
own
data,
setting
up
an
FTP
server
to
serve
medical
images
is
necessary.
This
approach
accommodates
the
large
size
of
medical
images,
which
are
typically
too
large
to
send
in
API
payloads
directly.

Running
NIM
Microservices
Locally

To
run
NIM
microservices
locally,
users
need
to
apply
for
NVIDIA
NIM
access.
Upon
approval,
they
will
receive
a
Docker
container
to
run
the
VISTA-3D
NIM
microservice
on
their
preferred
hardware.
Prerequisites
include
having
Docker,
Docker
Compose,
and
NVIDIA
drivers
installed.

A
sample
Docker
Compose
file
is
provided
to
help
users
get
started
quickly,
along
with
instructions
for
setting
up
an
NGINX
server
to
serve
images.

Conclusion

NVIDIA’s
VISTA-3D
foundation
model
represents
a
significant
advancement
in
medical
imaging,
offering
precise
segmentation
of
over
100
organs
and
various
diseases
in
CT
scans.
The
NVIDIA
NIM
microservice
simplifies
the
deployment
and
usage
of
this
powerful
model,
enhancing
the
workflow
and
accuracy
of
radiologists.

Interested
parties
can
apply
for
access
to
the
VISTA-3D
NIM
microservice
to
leverage
its
capabilities
on
their
hardware,
streamlining
their
medical
imaging
processes.

Image
source:
Shutterstock