BIE-PInCS @ NGCLE@e-Novia 15.11.2017

1. Bracco Filippo <filippo.bracco@mail.polimi.it>
Di Vece Chiara <chiara.divece@mail.polimi.it>
–Giovanni Mela
“Inserisci qui una citazione”.
BIE-PInCS
Brain Injury Evaluation through Pupillometry based on
INfrared Camera System
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NGC-like event @e-Novia
Milano
November 15, 2017

2. DReAMS
CONTEXT
Modern pupillometry has expanded the study and
utility of pupil responses in many new domains:
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Psychiatry
Clinical diagnosis
Neuromarketing

3. DReAMS
First cause of death in industrialized
countries among young people: 2M cases
per year, globally speaking[3]; imposes
considerable costs for health systems.
Traumatic Brain Injury
TRAUMATIC BRAIN INJURY
Medical evaluation of pupillary reflex is
affected by subjectivity, non-repeatability,
low accuracy [4]
[3]Source:Tagliaferri, Fernanda, et al. "A systematic review of brain injury epidemiology in Europe." Acta neurochirurgica 148.3 (2006): 255-268.
[4] Clinical Utility of an Automated Pupillometer in Patients with Acute Brain Lesion. Jeong Goo Park, M.D., Chang Taek Moon, M.D., Ph.D., Dong Sun Park, M.D., Sang Woo
Song, M.D. Department of Neurosurgery, Konkuk University Medical Center, Seoul, Korea
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4. DReAMS
SOLUTION
We propose an automated infrared
pupillometer for detection and evaluation
of TBI. Analysis is based on an integrated test
of pupillary light reflex and impaired eye
movement.
- Increase in evaluation precision
- Increase in cure quality
- Low cost
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5. DReAMS
COMPETITIVE ANALYSIS 5
Low cost
High cost
High
accurancy &
reflexes
Low
accurancy &
reflexes

6. DReAMS
METHODOLOGY 6
Video storage
Stimulation and frame acquisition
Razer Stargazer Depth-Sensing HD Webcam
PYNQ-Z1 Python Productivity for Zynq
Video analysis
Output video
FPGA-based acceleration

7. DReAMS
METHODOLOGY 7
Example of output video with pupillary features of clinical interest

8. DReAMS
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Load OpenCV image
Find ROI eyes
Equalize histogram
Threshold filter
Morphological transformation
Classification of contours
Approximation with circle
Algorithm overview

9. DReAMS
9
Hardware acceleration purpose
Load OpenCV image
Find ROI eyes
Equalize histogram
Threshold filter
Classification of contours
Approximation with circle
Synthesizable
Block
Morphological transformation

10. DReAMS
114
RESULTS: hardware execution time
0,0
0,4
0,8
1,2
1,6
+ overhead
time(ms)
HW SW
Mean time per ROI analysis
0
0,8
1,6
2,4
3,2
4
Mean time per frame
time(ms)
image analysed
unit of time
=
Performance indicator:
15
x3,19
x1,51 x1,62
10
97

11. DReAMS
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CONCLUSIONS
• Optimized ROI search (x3,47 speed-up)
• Reduced computation time using FPGA (x1,62 speed-up)
• Increased images analysed per sec (from 97 to 114)
• Low cost, portable, valuable for Point-of-Care analysis
FUTURE IMPROVEMENTS
• To receive the data stream directly from the camera
• Further shift of computation toward hardware

12. DReAMS
Team
Chiara Di VeceFilippo Bracco
filippo.bracco@mail.polimi.it chiara.divece@mail.polimi.it
Biomedical and Mechanical
Engineering M.Sc. student
Biomedical Engineering
M.Sc. student
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13. DReAMS
Thanks for your
attention
Feel free to contact us:
<chiara.divece@mail.polimi.it>
<filippo.bracco@mail.polimi.it>
<biepincs@gmail.com> https://www.facebook.com/BIEPInCSatNECST
https://twitter.com/BIEPInCSatNECST
https://www.slideshare.net/BIEPInCS
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