Stain Normalization using Sparse AutoEncoders (StaNoSA): Application to digital pathology

•Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image.•Validated using three expe...

Full description

Saved in:
Bibliographic Details
Published in:Computerized medical imaging and graphics Vol. 57; pp. 50 - 61
Main Authors: Janowczyk, Andrew, Basavanhally, Ajay, Madabhushi, Anant
Format: Journal Article
Language:English
Published: United States Elsevier Ltd 01.04.2017
Elsevier Science Ltd
Subjects:
Algorithms
Biopsy - methods
Breast - diagnostic imaging
Color
Deep learning
Diagnosis, Computer-Assisted - methods
Diagnostic systems
Digital histopathology
Histological Techniques
Histopathology
Humans
Image processing
Image Processing, Computer-Assisted - methods
Internal Medicine
Machine Learning
Other
Pathology, Clinical - methods
Stain Normalization
Staining and Labeling - methods
Standardization
Digital histopathology
Deep learning
Stain Normalization
Image processing
ISSN:0895-6111, 1879-0771, 1879-0771
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:•Digital histopathology slides have many sources of variance.•These variances can cause algorithms to perform erratically.•Stain Normalization using Sparse AutoEncoders (StaNoSA) in introduced.•It standardizes color distributions of a test image to a single template image.•Validated using three experiments with five other color standardization approaches. Digital histopathology slides have many sources of variance, and while pathologists typically do not struggle with them, computer aided diagnostic algorithms can perform erratically. This manuscript presents Stain Normalization using Sparse AutoEncoders (StaNoSA) for use in standardizing the color distributions of a test image to that of a single template image. We show how sparse autoencoders can be leveraged to partition images into tissue sub-types, so that color standardization for each can be performed independently. StaNoSA was validated on three experiments and compared against five other color standardization approaches and shown to have either comparable or superior results.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ObjectType-Undefined-3
ISSN:0895-6111
1879-0771
1879-0771
DOI:10.1016/j.compmedimag.2016.05.003