Post on 14-Mar-2020
transcript
Improving quality and
efficiency through
Artificial Intelligence
Angel Alberich-Bayarri, PhDalberich_ang@gva.es
angel@quibim.com1 Biomedical Imaging Research Group
La Fe Polytechnics and University Hospital2 QUIBIM SL
Outline
• Introduction – quality
• The needs
• Implementation
• Applications
• Wrap-up
Introduction
"Quality means doing it right when no one is looking”
Henry Ford
Introduction
• Doing it right:
”We all recognize that Artificial Intelligence, Imaging
Biomarkers and Smart Data will drive Radiology crucial
role in Precision Medicine.”
Introduction
• 2016 was the year of fear of AI
• In 2017 we have passed one of the most
significant ‘hypes’ in the last decades of
Radiology. AI was seen as the end of the
specialty
• 2018 should be the year of hope for AI in
Radiology
Now it is time to focus on real
needs in daily practice
Fear
Hype
Hope
Introduction
• Artificial Intelligence: Let the
computer learn from examples
• Machine Learning: Subset of
artificial intelligence algorithms
which allow computer to learn to
perform tasks given a labelled
dataset (supervised learning).
• Deep Learning: Subset of
Machine Learning algorithms
which use Neural Networks to
learn from large labelled
datasets.
Introduction
• Deep Neural Networks:
Computing model inspired in
biologic neural networks.
• Main Elements:– Input Layer
– Hidden Layer
– Output Layer
– Weights (Connections)
– Activation Functions
– Loss function Modify weights during training to
minimize the output cost function
Introduction
• Convolutional Neural Networks: Special type of neural
networks which present an outstanding performance in
Computer Vision problems.
• Key elements:– Convolutional Layers
– Pooling Layers
– Fully Connected Layers
In addition to connection weights,
convolutional filters are learnt during
training, which are able to recognize
patterns in images.
The needs
“Quantification has still not had an impact in
current Radiology workflows”
The needs
• Can we do this today with our PACS and workstations?
Challenge 1: Quantify automatically the volumes of brain regions (or
have them pre-computed), have a report with the results in PACS and
use hyppocampal volume for diagnosis/follow-up of mild cognitive
impairment
Challenge 2: Search in our PACS or IT system for cases with a CT-
derived emphysema percentage higher than 10% to include them in a
clinical trial for COPD
The needs
Technology is already available
The needs
therefore…
The needs
…where is the limitation?
The needs
INTEGRATION
Implementation
• ‘Seamless’ integration:
1. Cases are retrieved from the PACS automatically by
pre-defined rules (i.e. StudyDescription) at specific
times (i.e. night?)
2. Pre-computing: A.I. models or automated image
analysis ‘pipelines’ start execution upon reception if
a matching exists
3. Results are generated and sent back to PACS in
order to be ready for radiological reading
Mañas-García A. MIUC the new toolkit of QUIBIM Precision® platform to
beat traditional workstations. quibim.com/blog. 22-OCT-2017
Implementation
• Requirements for an AI software platform
Alberich-Bayarri A et al. Development of imaging biomarkers and
generation of big data. Radiol Med. 2017 ;122(6):444-448.
Implementation
• Requirements vs. Infrastructures
Alberich-Bayarri A et al. Development of imaging biomarkers and
generation of big data. Radiol Med. 2017 ;122(6):444-448.
Implementation
• Data protection
Develop engineering solutions to work only with
dissociated data in the Cloud
Example: Embedded DICOM anonymizers in hospital
‘connectors’
Implementation
• Cloud architecture
Implementation
• Hospital ‘connector’
Mañas-García A. MIUC the new toolkit of QUIBIM Precision® platform to
beat traditional workstations. quibim.com/blog. 22-OCT-2017
Applications
How can we apply this technology?
Applications
Applications
Quantitative Structured Reports:
From images to data
Applications
Mathworks Inc, Natick MA, USA
Applications
• Train-test process of a CNN
Convolutional Neural Network
(CNN)Input training data
Output
(Labels)
Weights and
filters tuning
‘Tuned’ Convolutional
Neural Network (CNN)
Input testing data
Previously ‘unseen’
Output
(Labeled
data)
TRAINING
TESTING
Slow
(h, d, m)
GPU
Computing
Fast (s) Consumer
devices
Labels
Applications
• The ’must’ for research in AI and segmentation:
NVIDIA Quadro GP100
HARDWARE SOFTWARE
DATA SCIENTISTS
LABELED DATA
Applications
Potential solutions
Seamless labeling integrated in radiologists workflow
Structured reporting
Pre-computed editable segmentations
Hiring experts for cases labeling (classification, region delineation)
Data augmentation
Transfer learning
Current problem: Lack of labeled data
Applications
• Automated prostate segmentation in new unseen cases
True positive
False negative
False positive
DICE COEFFICIENT:
Training: 91,25%
Validation: 82,47%
Test: 81,83%
Training: 50 cases (PROMISE12 challenge)
T2-FSE Axial + Whole gland masks
Ana Jiménez-Pastor
Rafael López González
Applications
Applications
• Automated liver MR segmentation in new unseen cases
True positive
False positive
False negative
DICE COEFFICIENT:
Training: 96.59%
Validation: 96.60%
Test: 95.60%
35 cases (own data)
LATE ENHANCEMENT THRIVE + whole liver masks
Ana Jiménez-Pastor
Applications
• Automated vertebra CT localization in new unseen cases
230 cases (own data)
Cervical-Thoracic-Abdomen-Pelvis CT scans with
arbitrary FOV + Labeled vertebrae centroids
Ana Jiménez-Pastor
Applications
• First-read of chest X-rays (research, ready in summer 2018)
Applications
• First-read of chest X-rays (research, ready in summer 2018)
• Plotting the features that the network identifies as most
characteristic
Implementation
Wrap-up
• New knowledge, like the relationship of Imaging
Biomarkers with clinical endpoints (survival, treatment
response, …) will only be generated with integration
of AI and Structured Reporting in Radiology
Departments
Let’s do it right!!!
Improving quality and
efficiency through
Artificial Intelligence
Angel Alberich-Bayarri, PhDalberich_ang@gva.es
angel@quibim.com1 Biomedical Imaging Research Group
La Fe Polytechnics and University Hospital2 QUIBIM SL