Prostate Cancer Biomarker Analysis
                     ®
On-Demand Digital Pathology OptraScan®
Affordable, Subscription-based System
Artificial Intelligence & Machine Learning based System for accurate, 
rapid and reproducible analysis of Prostate Cancer
Examination of histological specimens under the microscope by 
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a pathologist is one of the most reliable methods used in 
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detection of prostate cancer. This is carried out by examining the 
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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:
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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 
Gleason score 3+3=6. Grade 1
from Gleason Grading :
Gland Formation: Discrete, well formed, uniform large glands arranged back to back
Ø Fully automated solution :  End to end solution Legend:       Lumen       Epithelial nuclei       Epithelial cell cytoplasm
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 Gleason score  4+4=8. Grade 4 
structural, morphometric, texture, nucleo- Gland Formation: Fused, cribriform, poorly formed glands, punched out lumens 
Legend:       Lumen       Epithelial nuclei       Epithelial cell cytoplasm
cytoplasmic ratio and color features for 
detected cell nuclei and identified glandular 
regions.
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. Result: Gleason Score 5+5=10. Grade 5
Gland Formation: lacks gland formation,  Solid sheet of uniform neoplastic cells                                                     
m Object level tumor grading is done using 
Legend:       Epithelial nuclei
feature characteristics for malignant and 
benign cell nuclei like mean intensity, area, 
standard deviation of intensity etc. 
Ø Key Differentiator :  
m Easily retrainable machine learning system.
m High classification accuracy.
OptraScan® ®On-Demand Digital Pathology  Solutions 
OS-15 OS-120 OS-FS OS-FL
15-slide brightfield 120-slide brightfield 7-slide frozen sections, 15-slide fluorescence, 
with live view mode with 6 filter cubes 
IMAGEPath®
Web-based Image Management and Viewing
TELEPathTM OptraASSAYS
TM
Web and Mobile Digital Conferencing On-Demand Image Analysis
CLOUDPath®
Laboratory Information Management System
100 Century Center Court,
Suite 410, San Jose, CA 95112
OptraSCAN is an ISO13485 certified company *All OptraSCAN systems and solutions are for research use only