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