The document describes a cloud-based virtual microscopy system called VIRTUM. It allows uploading of various types of microscopy image files which are then processed using a library of 70+ image analysis algorithms running on GPUs. This enables fast visualization, measurement, segmentation and analysis of 2D and 3D microscopy images for applications in medicine, biology and other fields. Key benefits of VIRTUM include flexibility, accelerated processing, and intuitive workflow-based analysis of large image datasets in the cloud.

1. VIRTUAL
MICROSCOPY
IN
THE
CLOUD
WOJCIECH
TARNAWSKI
,
CSO
MICROSCOPEIT
LTD.
1
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

2. Virtual
Microscopy
in
the
Cloud
Wojciech
Tarnawski,
PhD,
CSO
MicroscopeIT
Ltd.,
Wroclaw,
Poland
2
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

3. MICROSCOPY
IS
COMPLICATED
! Different formats, different producers.
! Different software for different image processing tasks.
! Image analysis takes time.
! Open Source
vs.
Commercial Software.
! Image types: 2D (fluorescence, phase-contrast), 3D
(confocal), 4D (3D objects in time), different channels targeting
different molecular elements.
3
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL
CreaMve
Commons
2.0,
Nicole
Yeary's
photos
via
GeRy
Images

4. WHAT
IS
VIRTUM?
Cloud Computing
Image processing pipeline
integrated accessed in the web
browser.
Acceleration
Time consuming image analysis
ported to GPU.
Robust and fast workflow-based
image analysis
Save time thanks to intelligent
algorithms with „visual” development.
Image
credit:
leverhawk.com,
Why
is
cloud
integraMon
sMll
an
adopMon
barrier,
2012.
4
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL
Information Retrieval
Phenotype detection of biologically
relevant information directly from images.
Flexibility
All formats, dimensions and
modality supported

5.
IN
ACTION
Our
system:
32
GPU
cards
(6
donated
by
AMD)
Data
acquisi:on
Database
" Work-­‐flow
based
image
processing
and
task
scheduling
5
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

6. FEATURES,
APPLICATIONS
Visualization (Virtual Microscopy)
Medicine and
biology
Clinical trials
Scientific
research
E-learning
Teleconferencing
teleconsultations
Quantitative
data analysis
Biotechnology
High-Content and
High-Throughput
Screening
Data Analysis
6
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

7.
2D
Image
Series
Viewer
Visualiza:on
WSI
VisualizaMon
3D
Image
Series
Movie
ProjecMon
3D
Geometry
Rendering
ReconstrucMon
Input
Data
Types
Not Ordered
WSI
Image z-­‐stacks
Time-­‐Lapse Time-­‐Lapse
Images
( Image Pyramids)
Image Series
Z-­‐Stacks
7
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

8. INPUT
DATA
TYPES
:
NOT-­‐ORDERED
SETS
AND
TIME-­‐LAPSE
IMAGE
SERIES
8
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

9. INPUT
DATA
TYPES
:
Z-­‐STACKS
AND
TIME-­‐LAPSE
Z-­‐STACKS
9
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

10. INPUT
DATA
TYPES
:
IMAGE
PYRAMID
10
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

11.
2D
Image
Series
Viewer
Visualiza:on
WSI
VisualizaMon
3D
Image
Series
Movie
ProjecMon
3D
Geometry
Rendering
ReconstrucMon
Input
Data
Types
Not Ordered
WSI
Image z-­‐stacks
Time-­‐Lapse Time-­‐Lapse
Images
( Image Pyramids)
Image Series
Z-­‐Stacks
Image Processing and Analysis Library
2-­‐3D Mesurements Image Preprocessing
2-­‐3D Object SegmentaDon 2-­‐3D Object Analysis StaDsDcs
Data
Analysis
2D Image Processing 2-­‐3D Image ReconstrucDon Time-­‐Dependent Analysis Post-­‐Processing
and Analysis
11
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

12. CLOUD
COMPONENTS
(BACK-­‐END)
1/3
Image Processing and Analysis Library : about 70 methods tailored
for microscopy imaga data implemented on CPU and GPU
2-­‐3D Mesurements
Image Preprocessing : noise removal, contrast improvement, inhomogeneous
lighDng removal, opDcal deconvoluDon, 2-­‐3D Image SDtching, Histogram-­‐based
processing, MulD-­‐channel Image Composing, Image ArithmeDc, Edge DetecDon, …
etc.
2-­‐3D Object SegmentaDon : automaDc or machine-­‐learning methods for
segmentaDon of 2-­‐3D objects e.g. 2-­‐3D Cell Tracking Advanced SegmentaDon in
mulD-­‐dimensional space composed with texture and color features, AcDve Contour
and AcDve Mesh, Threshold -­‐ and Morphology – based SegmentaDon, Mean-­‐Shi[,
…
2-­‐3D Object Analysis : Split into 2-­‐3D Ellipsoids e.g. for highly clustered cells ,
Morphology Operatos , Weighted Distance Transform, Voronoi TriangulaDon, Object
RecogniDon module for Cell Phase ClassificaDon by Markov chains
12
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL
StaDsDcs Module – PCA, Basic StaDsDcs, Cluster Analysis,

13. 3D
IMAGE
SEGMENTATION
:
ACTIVE
MESH
13
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

14. CLOUD
COMPONENTS
(BACK-­‐END)
1/3
Image Processing and Analysis Library : about 70 methods tailored
for microscopy imaga data implemented on CPU and GPU
Workflow-­‐based
image
processing
*
A
Robust
Algorithm
for
Segmen:ng
and
Tracking
Clustered
Cells
in
Time-­‐Lapse
Fluorescent
Microscopy
Tarnawski,
W.
;
Kurtcuoglu,
V.
;
Lorek,
P.
;
Bodych,
M.
;RoRer,
J.
;
Muszkieta,
M.
;
Piwowar,
L.
;
Poulikakos,
D.
;Majkowski,
M.
;
Ferrari,
A.
Biomedical
and
Health
InformaMcs,
IEEE
Journal
of
Volume:
17
,
Issue:
4
PublicaMon
Year:
2013
,
Page(s):
862
-­‐
869
14
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

15. WORKFLOW
–
BASED
IMAGE
PROCESSING
AND
ANALYSIS
15
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

16. USE
CASES
! Detection of nuclei and cytoplasm in 80 000 images (512x512 pixels) takes about 2 hours on multicore CPU (AMD
Athlon(tm)
II
X4
640
Processor).
GPU provided up to 4x acceleration
! Optical deconvolution : about 25x acceleration for 512x512 image
! 3D-dimensional diffuse filter on image-stack (z-stack with 1920x1080) : about 10x acceleration
16
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

17. CLOUD
COMPONENTS
(BACK-­‐END)
2/3
Image Processing and Analysis Library : about 70 methods tailored
for microscopy imaga data implemented on CPU and GPU
Task Scheduler to provide image analysis results for many users.
Scheduling approach :
Scheduler –> Executor –> Worker –> Task
-­‐
Schedules
image
processing
tasks
on
the
CPU
&
GPU
cluster.
-­‐
Monitors
CPU,
GPU,
memory,
storage
usage.
-­‐
OpMmizes
scalability.
17
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

18. CLOUD
COMPONENTS
(BACK-­‐END)
3/3
Image Processing and Analysis Library : about 70 methods tailored for
microscopy imaga data implemented on CPU and GPU
Task Scheduler to provide image analysis results for many users.
Database Module -­‐ to store the microscopic image data
Database Module provides upload data module that supports:
• about 100 microscopic image data formats (i.e. lsm, nd2, oly, mulD-­‐
channel , 16-­‐bit Dff, basic graphic formats, …)
• compressed images series (zip)
• filename parser to upload image series ordered by channel, z-­‐stack
layers, Dme-­‐points, …
• users data are fully organized
• users can be assigned to many projects
18
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

19. PROJECT
DATA
ORGANIZATION
19
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

20. CLIENT
(GUI)
COMPONENTS
Graphical
User
Interface
(GUI)
installed
as
a
plugin
in
the
web
browser:
! Designed
for
touch-­‐based
devices.
! Designed
to
tag
microscopic
image
series
with
metadata.
! Includes
different
viewers
to
visualize
mulM-­‐dimensional
images.
! Provides
„visual”
interface
to
design
the
workflow
for
image
processing
and
analysis.
! Provides
tools
to
select
the
image
regions
for
futher
iamge
analysis.
20
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

21. ! MicroscopeIT
Ltd.
Kutnowska
1-­‐2
Wroclaw,
Poland
! Contact:
wojciech.tarnawski@microscopeit.com
Tel.
+48
605
111
445
Skype:tar_woj
21
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL

22. DISCLAIMER
&
ATTRIBUTION
The
informaMon
presented
in
this
document
is
for
informaMonal
purposes
only
and
may
contain
technical
inaccuracies,
omissions
and
typographical
errors.
The
informaMon
contained
herein
is
subject
to
change
and
may
be
rendered
inaccurate
for
many
reasons,
including
but
not
limited
to
product
and
roadmap
changes,
component
and
motherboard
version
changes,
new
model
and/or
product
releases,
product
differences
between
differing
manufacturers,
sotware
changes,
BIOS
flashes,
firmware
upgrades,
or
the
like.
AMD
assumes
no
obligaMon
to
update
or
otherwise
correct
or
revise
this
informaMon.
However,
AMD
reserves
the
right
to
revise
this
informaMon
and
to
make
changes
from
Mme
to
Mme
to
the
content
hereof
without
obligaMon
of
AMD
to
noMfy
any
person
of
such
revisions
or
changes.
AMD
MAKES
NO
REPRESENTATIONS
OR
WARRANTIES
WITH
RESPECT
TO
THE
CONTENTS
HEREOF
AND
ASSUMES
NO
RESPONSIBILITY
FOR
ANY
INACCURACIES,
ERRORS
OR
OMISSIONS
THAT
MAY
APPEAR
IN
THIS
INFORMATION.
AMD
SPECIFICALLY
DISCLAIMS
ANY
IMPLIED
WARRANTIES
OF
MERCHANTABILITY
OR
FITNESS
FOR
ANY
PARTICULAR
PURPOSE.
IN
NO
EVENT
WILL
AMD
BE
LIABLE
TO
ANY
PERSON
FOR
ANY
DIRECT,
INDIRECT,
SPECIAL
OR
OTHER
CONSEQUENTIAL
DAMAGES
ARISING
FROM
THE
USE
OF
ANY
INFORMATION
CONTAINED
HEREIN,
EVEN
IF
AMD
IS
EXPRESSLY
ADVISED
OF
THE
POSSIBILITY
OF
SUCH
DAMAGES.
ATTRIBUTION
©
2013
Advanced
Micro
Devices,
Inc.
All
rights
reserved.
AMD,
the
AMD
Arrow
logo
and
combinaMons
thereof
are
trademarks
of
Advanced
Micro
Devices,
Inc.
in
the
United
States
and/or
other
jurisdicMons.
SPEC
is
a
registered
trademark
of
the
Standard
Performance
EvaluaMon
CorporaMon
(SPEC).
Other
names
are
for
informaMonal
purposes
only
and
may
be
trademarks
of
their
respecMve
owners.
22
|
PRESENTATION
TITLE
|
NOVEMBER
21,
2013
|
CONFIDENTIAL