ISS Art shares experience of using Computer Vision in neuropharmacology on the example of the portfolio project. ISS Art CTO describes the technical part of developing a Computer Vision script for neuropharmacology at HappyDevLite conference http://happydevlite.1der.link/site. You will know about the script we wrote for the company that creates drugs for diseases related to the death of nerve cells. Well-known examples of these diseases are Parkinson and Alzheimer. What details are disclosed: -how to identify an object that move a bit -how to identify the core of the object (in our case it was the core of a cell) -how highlight an object and to identify the affiliation etc.

1. The use of methods of
computer vision in
neuropharmacology
Sergey Maslov, CTO, ISS Art

2. Student
Student+programmer
Teacher
Tester(didn’t like it)
Programmer
Head of development team
Architect
CTO
2
About me
Omsk State Polytechnical University

3. Customer
A company from the USA which
researches and creates drugs for
illnesses connected with nerve cell
death (e.g. Alzheimer, Parkinson
diseases).
3
Neuropharmacology - the field of development of
drugs for diseases of the nervous system.

4. Process
4
Grow cells in Petri
dishes
Drip drugs
Watch what
happens with cells

5. Watching cells
5
Digital microscope
Immunofluorescence
Automatic monitoring of
cell conditions (alive-dead)
process of immunofluorescence

6. Example of photo
Original photo from
digital microscope
6
Photo after pre-
processing
Shown for visibility

7. Nerve cell
structure of nerve cell
(from school book of biology)
7
Photo after pre-
processing
Main criteria of condition:
Brightness, presence of neurites

8. Issues
8
Customer’s tools:
Slow (2-3 hours for
processing 1 photo)
Inaccurate

9. We use
9
Brain
Mathematics
Programming

10. Pre-processing
10
Photo after pre-
processing
Make it black-and-white
Brightness/ Contrast

11. 11
Finding cell centers
Found cells
Classical computer
vision technique: Blob
detection
For geeks: bright spot - is local
maximum of Laplace transform
from Gauss blurring.
Must know: divergence of vector
field

12. 12
Finding neutrites
Ridge line, actually
Color - the height
Natural analogue:
ridge line
Photo after pre-
processing
Must know: classification of quadric surfaces,
eigenvalues of matrix of second derivatives

13. Watershed algorithm
13
Neurites affiliation to cells
algorithm working scheme

14. 14
Neurites affiliation to cells
kind of

15. 15
Input - output
Collection of daily
photos
Daily cell conditions
Quantity and length of
neurites
Chart

16. 16
Results
Faster (2-3 minutes instead of 2-3 hours per
photo)
Much more accurate (hard to say in numbers)
Use of indicators that were not used before
(quantity and length of neurites)
Tools for analyzing cell photos:
further cooperation with the customer

17. What to read
17
Nerve cells
Immunofluorescence
Opencv tutorial
Mathematics, programming and
English - study on your own :)
For those who is too lazy to google

18. Thank you!
Sergey Maslov, CTO, ISS Art LLC
Email: crm@issart.com