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CAM in Focus
A Deep Learning Dive into Image Analysis for
Biomedical Advancements
Philipp Kainz, CEO KML Vision
Challenges in Bioimage Analysis
Lack of Expert
Resources
Low
Reproducibility
Increased Data
Complexity
Large Data
Volumes
Parallel Projects and
Time Pressure
The Power of Deep Learning
Fast and Reproducible
Quantitative Results
Efficient Processing
of Large Datasets
Identification of Subtle
Patterns and Features
Reduced Human
Bias
Frequent Application
Upgrades
Applications and Challenges
Angiogenesis
Study anti/angiogenic effects
by quantifying vasculature
dynamics.
Tumor Research
Personalize cancer therapy by
implanting patient-derived cells or
cell lines onto the CAM.
Regenerative Medicine
Advance tissue engineering by
assessing biomaterials before
clinical use.
3R Approach
Promote animal-free research
practices by using the Replace,
Reduce, Refine principle.
The IKOSA Platform
Essential Tooling in CAM Research
● State-of-the-Art All-in-One Solution
● No Coding Skills Required
● User Collaboration
● Region of Interest Support
● Broad Range of Imaging Formats
Supported: 2D, time-series,
Z-stacks & multichannel images
● Train your own AI Apps (e.g. Cell
Profiling, Biomarkers, …)
● total area
● total length
● mean thickness, and
● the number of branching
points of blood vessels.
5 Steps to a Successful CAM Analysis
Upload your digital
image/slide data
into IKOSA
Invite project members
to work with the data
Start the CAM Assay automated
analysis on selected images or
regions of interests
Review results visualizations
and quantitative output
Run statistical
analysis and discuss
it with your team!
1
2
3
4
5
Future Directions of CAM
Retraining the IKOSA CAM assay
application improves in-ovo
experiments.
Three different CAMs and engrafted
AS-M cell line tumors.
Image adapted from the publication by the University of
Regensburg and Deggendorf Institute of Technology.
Licensee MDPI, Basel, Switzerland.
Kuri et al. (2022). Cells, 11(15), 2321. https://doi.org/10.3390/cells11152321
Original IKOSA CAM v1.0.0 IKOSA CAM v3.0.0 Original IKOSA CAM v1.0.0 IKOSA CAM v3.0.0
Quantify Beyond Vessels using AI
H&E staining of an AS-M cell line tumor after
removal from the CAM after one week.
Image from the publication by the University of Regensburg and
Deggendorf Institute of Technology.
Licensee MDPI, Basel, Switzerland.
Kuri et al. (2022). Cells, 11(15), 2321. https://doi.org/10.3390/cells11152321
Use tumor material to create histology images
and investigate morphological features.
Use custom-trained AI apps for cell counting,
stain quantification, landmark detection, and
many more readouts.
Collaborative Work Builds Future
Become one of the renowned
CAM App Users!
● FAU Erlangen-Nürnberg, Germany
● University Medical Center Mainz, Germany
● University of Regensburg, Germany
● University of Liverpool, UK
● Medical University of Vienna, Austria
● Medical University of Graz, Austria
● University of Bari Aldo Moro, Italy
● LUM University Giuseppe Degennaro, Italy
● University of Padova, Italy
● Hong Kong Polytechnic University, China
● Hong Kong Baptist University, China
MPM tumour nodules remodel surrounding CAM
vasculature.
Image adapted from the publication by the University of Liverpool.
Licensee MDPI, Basel, Switzerland
Barnett et al. (2022). Cancers, 14(23), 5836. https://doi.org/10.3390/cancers14235836
KML Vision GmbH
Nikolaiplatz 4
8020 Graz
Austria
www.kmlvision.com
office@kmlvision.com
Thank You - Questions?
ikosa@kmlvision.com
+43 680 156 7596
Additional materials
CAM Assay Application Overview (link)
Technical Documentation about the CAM Assay
Application (link)
10 CAM Assay Publications to advance your
research (link)
Interview (link) and podcast (link) with Dr. Sarah
Barnett from University of Liverpool
Other CAM-related materials (link)

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Advancing CAM Assay Image Analysis Using Deep Learning Software

  • 1. CAM in Focus A Deep Learning Dive into Image Analysis for Biomedical Advancements Philipp Kainz, CEO KML Vision
  • 2. Challenges in Bioimage Analysis Lack of Expert Resources Low Reproducibility Increased Data Complexity Large Data Volumes Parallel Projects and Time Pressure
  • 3. The Power of Deep Learning Fast and Reproducible Quantitative Results Efficient Processing of Large Datasets Identification of Subtle Patterns and Features Reduced Human Bias Frequent Application Upgrades
  • 4. Applications and Challenges Angiogenesis Study anti/angiogenic effects by quantifying vasculature dynamics. Tumor Research Personalize cancer therapy by implanting patient-derived cells or cell lines onto the CAM. Regenerative Medicine Advance tissue engineering by assessing biomaterials before clinical use. 3R Approach Promote animal-free research practices by using the Replace, Reduce, Refine principle.
  • 5. The IKOSA Platform Essential Tooling in CAM Research ● State-of-the-Art All-in-One Solution ● No Coding Skills Required ● User Collaboration ● Region of Interest Support ● Broad Range of Imaging Formats Supported: 2D, time-series, Z-stacks & multichannel images ● Train your own AI Apps (e.g. Cell Profiling, Biomarkers, …) ● total area ● total length ● mean thickness, and ● the number of branching points of blood vessels.
  • 6. 5 Steps to a Successful CAM Analysis Upload your digital image/slide data into IKOSA Invite project members to work with the data Start the CAM Assay automated analysis on selected images or regions of interests Review results visualizations and quantitative output Run statistical analysis and discuss it with your team! 1 2 3 4 5
  • 7. Future Directions of CAM Retraining the IKOSA CAM assay application improves in-ovo experiments. Three different CAMs and engrafted AS-M cell line tumors. Image adapted from the publication by the University of Regensburg and Deggendorf Institute of Technology. Licensee MDPI, Basel, Switzerland. Kuri et al. (2022). Cells, 11(15), 2321. https://doi.org/10.3390/cells11152321 Original IKOSA CAM v1.0.0 IKOSA CAM v3.0.0 Original IKOSA CAM v1.0.0 IKOSA CAM v3.0.0
  • 8. Quantify Beyond Vessels using AI H&E staining of an AS-M cell line tumor after removal from the CAM after one week. Image from the publication by the University of Regensburg and Deggendorf Institute of Technology. Licensee MDPI, Basel, Switzerland. Kuri et al. (2022). Cells, 11(15), 2321. https://doi.org/10.3390/cells11152321 Use tumor material to create histology images and investigate morphological features. Use custom-trained AI apps for cell counting, stain quantification, landmark detection, and many more readouts.
  • 9. Collaborative Work Builds Future Become one of the renowned CAM App Users! ● FAU Erlangen-Nürnberg, Germany ● University Medical Center Mainz, Germany ● University of Regensburg, Germany ● University of Liverpool, UK ● Medical University of Vienna, Austria ● Medical University of Graz, Austria ● University of Bari Aldo Moro, Italy ● LUM University Giuseppe Degennaro, Italy ● University of Padova, Italy ● Hong Kong Polytechnic University, China ● Hong Kong Baptist University, China MPM tumour nodules remodel surrounding CAM vasculature. Image adapted from the publication by the University of Liverpool. Licensee MDPI, Basel, Switzerland Barnett et al. (2022). Cancers, 14(23), 5836. https://doi.org/10.3390/cancers14235836
  • 10. KML Vision GmbH Nikolaiplatz 4 8020 Graz Austria www.kmlvision.com office@kmlvision.com Thank You - Questions? ikosa@kmlvision.com +43 680 156 7596 Additional materials CAM Assay Application Overview (link) Technical Documentation about the CAM Assay Application (link) 10 CAM Assay Publications to advance your research (link) Interview (link) and podcast (link) with Dr. Sarah Barnett from University of Liverpool Other CAM-related materials (link)