scch is an initiative of scch is located in from image analysis research to software for clinical,...

SCCH is an initiative of SCCH is located in

From image analysis research to softwarefor clinical, biomedical and healthcare environments

Dr. Julian Mattes

+43 7236 3343 [email protected]

http://www.scch.at/

2© Software Competence Center Hagenberg GmbH, MATTES Medical Imaging GmbH

Introduction


Introduction


Aortic stent grafts

Endovascular treatment of an aortic aneurysm

Abdominal aortic aneurysm

Stentgraft thoracic

Stentgraft abdominal


Medical Problem

Complications due to a stentgraft

Problems: Leak by (local) „displacement“ Leak by fatigue of material Other leaks Occlusions due to kinks

Reasons: Changes in the aneurismatic sac Elongation of the aorta Technical fatigue (local) displacement Technical fatigue leak



Medical Problem

Data / Aim of the analysis


1 Patient, 2 CT volume scans, interval: 11 months

Goal:Recognition of an initiating complication

Individual risk assessment Preventive endovascular re-intervention


Software Solution

Approach for early recognizing complications

Software for

A highly automated generation of the stent‘s surface

Basis for 3D-Measurements and superimpositions

Superimposition of the stent surface for different points

in time

Representation of the migration with respect to a

reference structure


Software Solution

Deformation analysis

Superimposition of the stent‘s surface for different points in time

Quantitative value for the amount of deformation

Mean length of motion vectors: 5,65 mm


State / Further requirements

Software

allows reproducing conventionally measured parameter with

the same precision and reliability (J.Mattes et al. EJR 2012 81(3)496-501,Epub 2011)

helps avoiding to overlook complications

helps better recognizing and assessing initiating complica-

tions second reader for a preventive re-intervention

Requirements for industrial exploitation

a study of a larger patient population

Integration of additional anatomical structures

Minimal user effort, integration in the radiologist’s work flow

Image analysis application fields

Röntgen

MR

USPET

Pre-operative planning of paths of surgical tools (e.g., on MRI). Markers are fixed on patient and visible in the pre-operative image.

During operation the fixed markers are localized by the same system as the diodes on the surgical tool. They are co-registered with the markers on the pre-operative image.

“Seminal paper” (Russ Taylor): Benabid, Cinquin, Lavallee, Le Bas, Demongeot, Rougement (1987): A computer driven robot for stereotactic surgery connected to CAT-scan magnetic resonance imaging. Technological design and preliminary results. Applied neurophysiology, 50: 153-154

Computer aided diagnosis

www.praxim.fr

Computer assisted interventions: planning, navigation

12© Software Competence Center Hagenberg GmbH, 2015

Overview

Introduction Example: Shape change of an implant – aortic stent grafts

General aspects Innovation chain Exploitation strategy Market Software quality

Further examples Image analysis in histopathology Cellular motion and anti-cancer drug investigation A foot scanner technology


General aspects

14

Innovation chain

Reproduced from Tirillum Power Wind Corporation


Innovation chain

Example stent grafts Technology push new technique non-rigid surface registration Market pull need for quantification tool, integrated in work flow

Reproduced from Tirillum Power Wind Corporation


Exploitation strategy

Development on demand (market pull) Example foot scanner

Development on own initiative Often driven by technology push Within subsidized project (possibly with industrial partners) Own patents Exploitation via royalties for software, patents Combination with services Exploitation via start up

Patents Software patents??


Market

Knowledge about market Competitors / potential partners State of products Size of market Framework requirements Market entry barriers (certification, access to PACS)


Software quality

Certification EU directive for medical products (EN 62304, EN ISO 13485,

EN ISO 14971, EN 62366) Software development life cycle (software development

process) Requirement gathering and analysis (documentation of

specifications) Design Implementation or coding Testing Deployment Maintenance

Documentation, review


Examples


Examples

o Shape change of an implant – aortic stent grafts Image analysis in histopathology Cellular motion and anti-cancer drug investigation A foot scanner technology

21© Software Competence Center Hagenberg GmbH

Image analysis in histopathology

Project TisQuant: Procedures for highly automated assessment and classification of cells in tissue sections by means of spatial marker profiles

Challenge: Association of individual cells and stained cellular markers

Assessment of drug treatment Assessment of tumor agressivity 7 partners from Germany and Austria

3 scientific partners: SCCH, University of Vienna, DKFZ (German Cancer Research Center)

4 SMEs (2 from Germany and 2 from Austria)

2 partners are end users (DKFZ and the SME LabDia from Vienna)

TisQuant – Key components


Technical / Image analysis Automated cell detection/segmentation User interaction work flows Generation of ground truth Quatitative analysis and classification

Experimental research Identification and establishing of a membrane marker

System integration Integration of the system of 3 SME partners Testing under lab conditions

Neuroblastoma, IHC stained bright field image (part)

Acquired within TisQuant (Labdia, TG)


Automated cell segmentation

Detektion/Segmentierung Zellkerne – klassisch histologisch

ForegroundMap

Binary Image(Cell Clumps)

Final Segmentation

FG / BGSegmentation

HistologicalImage (RGB)

Recursive ClumpDecomposition



Detektion/Segmentierung Zellkerne – klassisch histologisch

Dorfer et al. (2016) Submission to ISBI in preparation



Detektion/Segmentierung Zellkerne – klassisch histologisch Recursive Waterflow

Dorfer et al. (2016) Submission to ISBI in preparation

Zerlegungs-Baum erzeugt durch rekursives Clump-Splitting

User interaction work flows


Determination of segmentation parameters

Classification of cells

Supported ground truth labelling

Kromp et al. (2015) Computer Vision Winter Workshop Schloss Seggau, 2015

European Patent Pending


Examples

o Shape change of an implant – aortic stent graftso Image analysis in histopathology Cellular motion and anti-cancer drug investigation A foot scanner technology


Cellular motion

Cellular motion is relevant for the metastatic risk related to a tumour wound healing processes in developmental biology

Anti-migratory anti-cancer strategies reduced cellular motion as a response to an anti-cancer drug as

cetuximab increased cellular motion when the epidermal growth factor

(EGF) binds to its receptor on the cell membrane cetuximab blocks this binding

Investigating the mechanism of the motile behaviour of cells How alters EGF the motile behaviour of a cell?


CANCERMOTISYS

Cellular motion in the project

“ CANCERMOTISYS – Systems Biology of drug effects on the motility of gastric cancer cell lines“

www.cancermotisys.eu

Quantitative motion analysis:The epidermal growth factor (EGF) is stimulating the motile behavior of cancer cells. This can be prohibited by the anti-body cetuximab. In order to demonstrate such differences in motion precise quantitative measurements are necessary.

Molecular basis of cellular motion:Differences in gene expression are correlated to changes in the motile behavior

http://www.cancermotisys.eu/

30

Cell detection – problem

Cell detection in two problematic situations:1. Cells may agglomerate and form clusters.2. Cells may have a complex appearance with high variation in

between cells.

© Software Competence Center Hagenberg 2012

Input-Data: IAPA, TU München, AG Prof. B. Luber Gastric carcinoma cell line MKN1

Cluster of 5 cells.

Cell with complex appearance.

31

Cell detection – approach

Cell detection – an approach based on machine learning and texture partitioning


Input data

Texture partitioning Filtered and joined seeds Evaluation – blue: detections, yellow: ground truth

Dark seedsBright seeds

T. Kazmar et al. 32nd IC IEEE EMBS 2010

32

Cell detection – results


Results – including very difficult cases: If a cell is automatically labelled this is correct in 91% of the cases (precision) 83% of all cells are correctly detected (recall)

Evaluation on more than 300 single images with up to 80 cells each.

Results:

: all cells labelled manually

: automated detection

: cells not detected

: over-detection

All 5 cells have been correctly detected; no over-detection.

Correct detection of the cell with complex appearance; no over-detection.

Input-Data: IAPA, TU München, AG Prof. B. Luber Gastric carcinoma cell line MKN1

33

Motion parameters

© Software Competence Center Hagenberg GmbH 33

Radial EffectivityMean velocity Mean area

Motion parameters derived from cell tracking.

Time resolved parameters

Instantaneous velocity


Examples

o Shape change of an implant – aortic stent graftso Image analysis in histopathologyo Cellular motion and anti-cancer drug investigation A foot scanner technology

BootDoc foot analyzer

Device for measuring foot sizes based on photograph Aim

no cover requested, works if client leaves socks on

Previous device


dark field

Solution: New hardware Dark field around feet LED lights in stripes (red, white) Fish-eye lens Allows keeping box small

Software fast image analysis based on

MSER detection varying exposure time to get

the best results possible life video stream is available

unlike with competing scanners

length = 268.9/264.0 mmfront width = 98.8/99.9 mmheel width = 74.3/74.0 mmfoot type = 8.3/8.7 mm

naked feetblue/red/green socks

image undistortionbackground suppression

color adjustmentcandidate contour detection

best contour selectionprecise measurementsfoot image extraction



Works with or without socks!

automatic measurements length front width heel width foot type (pronation/supination)

precision under +-1.5mm disadvantages of the predecessor model solved

uncomfortable foot cover removed analysis even for feet with socks on mirror-less construction with no interfering

reflections large area for feet available smaller box thanks to fish-eye lens used

40© Software Competence Center Hagenberg

Conclusion

Mixture of technology push and market pull

Question task formulated by biologist/user

Precise and verificable requirement specification

Exploitation model royalties

Good market knowledge, company partner

Accept user interaction but keep it efficient

41© Software Competence Center Hagenberg

Acknowledgment

Universitätsklinik für Radiodiagnostik, Innsbruck

Iris Steingruber-Chemelli, Andreas Chemelli, Werner Jaschke

Institut für allgemeine Pathologie und pathologische Anatomie, Technische Universität München (München)

Birgit Luber, Julian Kneisel, Simone Keller

Labdia Labordiagnostik GmbH

Peter Ambros, Florian Kromp

BootDoc/Wintersteiger GmbH

Andreas Lachinger

Software Competence Center Hagenberg

Tomas Kazmar, Matej Smid, Matthias Dorfer

scch is an initiative of scch is located in from image analysis research to software for clinical,...

Documents

julian mattes

analysis stentgraft

stentgraft problems

different stentgraft

preoperative image

image analysis research

stent surface

different points