Prostate Cancer Biomarker Analysis- OptraSCAN

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On-Demand Digital Pathology
Affordable, Subscription-based System
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OptraScan
Artificial Intelligence & Machine Learning based System for accurate,
rapid and reproducible analysis of Prostate Cancer
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Examination of histological specimens under the microscope by
a pathologist is one of the most reliable methods used in
detection of prostate cancer. This is carried out by examining the
glandular architecture of the specimen by the most common
method for histological grading of prostate tissue - the Gleason
Grading System.
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The cancer tissue is classified from 1 to 5 grades; however, in the
recent times, this common method is found to be ineffective,
reason being:
Ø Analysis on visual interpretation lacks reproducibility
Ø It is limited by intra- and inter-pathologist variability
Our Machine-based scoring algorithms
Our solutions to resolve the challenges appearing
from Gleason Grading :
Ø Fully automated solution : End to end solution
with robust and efficient algorithm modules.
m Intelligent Segmentation module that works
on human perceptible color spaces to detect
cell nuclei based on recognizable patterns like
area, shape, intensity etc.
m Automatic detection of glandular lumens
based on the clustering of identified cell
nuclei and other features.
m Robust feature extraction module to extract
structural, morphometric, texture, nucleocytoplasmic
ratio and color features for
detected cell nuclei and identified glandular
regions.
Gleason score 3+3=6. Grade 1
Gland Formation: Discrete, well formed, uniform large glands arranged back to back
Legend: Lumen Epithelial nuclei Epithelial cell cytoplasm
Gleason score 4+4=8. Grade 4
Gland Formation: Fused, cribriform, poorly formed glands, punched out lumens
Legend: Lumen Epithelial nuclei Epithelial cell cytoplasm
Ø ANN (artificial neural network) based classifier :
m Feature fusion and feature ranking
techniques for representation to the Neural
network based classifier.
m The classifier is trained to distinguish
between moderately and poorly differentiated
glands.
m Object level tumor grading is done using
feature characteristics for malignant and
benign cell nuclei like mean intensity, area,
standard deviation of intensity etc.
Result: Gleason Score 5+5=10. Grade 5
Gland Formation: lacks gland formation, Solid sheet of uniform neoplastic cells
Legend: Epithelial nuclei
Ø Key Differentiator :
m Easily retrainable machine learning system.
m High classification accuracy.

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OptraScan
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On-Demand Digital Pathology Solutions
OS-15
15-slide brightfield
OS-120
120-slide brightfield
OS-FS
7-slide frozen sections,
with live view mode
OS-FL
15-slide fluorescence,
with 6 filter cubes
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IMAGEPath
Web-based Image Management and Viewing
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TELEPath
Web and Mobile Digital Conferencing
TM
OptraASSAYS
On-Demand Image Analysis
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CLOUDPath
Laboratory Information Management System
OptraSCAN is an ISO13485 certified company
100 Century Center Court,
Suite 410, San Jose, CA 95112
*All OptraSCAN systems and solutions are for research use only