Blog
The latest news from Google AI
Using Deep Learning to Facilitate Scientific Image Analysis
Friday, March 16, 2018
Posted by Samuel Yang, Research Scientist, Google Accelerated Science Team
Many scientific imaging applications, especially microscopy, can produce terabytes of data per day. These applications can benefit from recent advances in computer vision and deep learning. In our work with biologists on
robotic microscopy applications
(e.g., to
distinguish cellular phenotypes
) we've learned that assembling high quality image datasets that
separate signal from noise
is a difficult but important task. We've also learned that there are many scientists who may not write code, but who are still excited to utilize deep learning in their image analysis work. A particular challenge we can help address involves dealing with out-of-focus images. Even with the autofocus systems on state-of-the-art microscopes, poor configuration or hardware incompatibility may result in image quality issues. Having an automated way to rate focus quality can enable the detection, troubleshooting and removal of such images.
Deep Learning to the Rescue
In “
Assessing Microscope Image Focus Quality with Deep Learning
”, we trained a deep neural network to rate the focus quality of microscopy images with higher accuracy than previous methods. We also integrated the pre-trained
TensorFlow
model with plugins in
Fiji (ImageJ)
and
CellProfiler
, two leading open source scientific image analysis tools that can be used with either a graphical user interface or invoked via scripts.
A pre-trained TensorFlow model rates focus quality for a montage of microscope image patches of cells in
Fiji (ImageJ)
. Hue and lightness of the borders denote predicted focus quality and prediction uncertainty, respectively.
Our
publication
and source code (
TensorFlow
,
Fiji
,
CellProfiler
) illustrate the basics of a machine learning project workflow: assembling a training dataset (we synthetically defocused 384 in-focus images of cells, avoiding the need for a hand-labeled dataset), training a model using data augmentation, evaluating generalization (in our case, on unseen cell types acquired by an additional microscope) and deploying the pre-trained model. Previous tools for identifying image focus quality often require a user to manually review images for each dataset to determine a threshold between in and out-of-focus images; our pre-trained model requires no user set parameters to use, and can rate focus quality more accurately as well. To help improve interpretability, our model evaluates focus quality on 84×84 pixel patches which can be visualized with colored patch borders.
What about Images without Objects?
An interesting challenge we overcame was that there are often "blank" image patches with no objects, a scenario where no notion of focus quality exists. Instead of explicitly labeling these "blank" patches and teaching our model to recognize them as a separate category, we configured our model to predict a probability distribution across defocus levels, allowing it to learn to express uncertainty (dim borders in the figure) for these empty patches (e.g. predict equal probability in/out-of-focus).
What's Next?
Deep learning-based approaches for scientific image analysis will improve accuracy, reduce manual parameter tuning and may reveal new insights. Clearly, the sharing and availability of
datasets and models
, and implementation into tools that are proven to be useful within respective communities, will be important for widespread adoption.
Acknowledgements
We thank Claire McQuin, Allen Goodman,
Anne Carpenter
of the Broad Institute and
Kevin Eliceiri
of the University of Wisconsin at Madison for assistance with CellProfiler and Fiji integration, respectively.
Labels
2018
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Robotics
schema.org
Search
search ads
Security and Privacy
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2019
Jun
May
Apr
Mar
Feb
Jan
2018
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2017
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2016
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2015
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2014
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2013
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2012
Dec
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2011
Dec
Nov
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2010
Dec
Nov
Oct
Sep
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2009
Dec
Nov
Aug
Jul
Jun
May
Apr
Mar
Feb
Jan
2008
Dec
Nov
Oct
Sep
Jul
May
Apr
Mar
Feb
2007
Oct
Sep
Aug
Jul
Jun
Feb
2006
Dec
Nov
Sep
Aug
Jul
Jun
Apr
Mar
Feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.
