Transforming images into meaningful data through AI and...

transcript

Transforming images into meaningful

data through AI and imaging biomarkersAngel Alberich-Bayarri, PhD

alberich_ang@gva.es

angel@quibim.com1 Biomedical Imaging Research Group (GIBI230)

La Fe Polytechnics and University Hospital2 QUIBIM SL

Content

1. Introduction

2. Steps to meaningfulness1. Acquisition

2. Processing

3. Analysis

4. Interpretation - Radiomics

3. Implementation

4. Topics for discussion

Introduction

• Current information systems do not allow the appropriate

management of image annotations, segmentation masks

and quantitative data, among others.

• There still exists a high variability between imaging

vendors, both in the image quality characteristics and in

the measurements generated in different workstations.

• AI is usually proposed as the solution for everything but it

is really efficient when designed to solve highly specific

problems

Introduction

• Challenges of AI and Quantitative imaging solutions in

Medicine

Objective and reproducibleEfficacy in the evaluation of the disease and treatment

Cost-efficientProviding surrogate results or

close to clinical endpoints

Image analysis methods

Meaningfulness

“The quality of having great value or

significance”

Steps to meaningfulness

1Martí Bonmatí L, Alberich-Bayarri A, et al. [Imaging biomarkers, quantitative imaging, and

bioengineering]. Radiologia. 2012 May-Jun;54(3):269-78.

• Stepwise strategy for the extraction of quantitative

information from medical images

European Society of Radiology. ESR statement on the stepwise

development of Imaging Biomarkers. Insights Imaging. 2013 Apr;4(2):147-52.

IMAGE ACQUISITION OPTIMIZATION

IMAGE PROCESSINGSEGMENTATION

REGISTRATION

IMAGE ANALYSIS

INTERPRETATION, RADIOMICS

Our approach:

AI is not the focus, but the tool to

optimize our workflows when

needed

METROLOGY

• Acquisition

Calibration and phantoms are a must

• Acquisition

CT - HU MR – T1 & T2 CT and MR, porous

structures

• Processing

Beyond image classification and

semantics, segmentation is the

main part of the quantitative

imaging workflows that will benefit

from AI

• Computing model inspired by

biological neural networks.

• Key elements:

o Input Layer

o Hidden Layers

o Output Layer

o Connections (Weights)

• Modify the intensity of its

connections (weights) during

training with labeled data

Neural Networks

• Special type of neural networks which present an outstanding

performance in Computer Vision problems.

• Key elements:

o Convolutional Layers

o Pooling Layers

o Fully Connected Layers

• In addition of fully connected weights, convolutional filters are learnt

during training, which are able to recognize patterns in images.

Convolutional Neural Networks (CNN)

True positive

False negative

False positive

DICE COEFFICIENT:

Training: 91,25%

Validation: 82,47%

Test: 81,83%

• Prostate segmentation

50 cases

True positive

False positive

False negative

DICE COEFFICIENT:

Training: 96.59%

Validation: 96.60%

Test: 87.37%

• Liver segmentation

35 cases

• Analysis: Prostate cancer

Source images

Intensity

Textures

Function

(~200-500)Massive analysis of Imaging

Biomarkers

Imaging Data Mining

• Analysis: Prostate cancer

– Combine infromation from

cellularity (MR diffusion) and

tumoral angiogenesis (MR

perfusion) to determine regions

with higher tumoral

aggresiveness

– Registration:• Afine + B-Splines (elastic)

• Mutual information

T2 Diffusion(register)

Perfusion(register)

Deformation

• Analysis: Prostate cancer– Diffusion and perfusion analysis

– Voxelwise data clustering

– Hierarchy aglomerative clustering

– Z-score

ADC [mm2/s]

ADC [z-score]

• Reports

• Analysis: Prostate cancerG

• Data reduction

Baseline Follow-up

• Experimental treatment (40 patients)

Publication pending

• Data reduction

• Experimental treatment (40 patients)

• Prognostic value:– Relapse

– D2D at baseline (p=0.05)

Publication pending

2D Fractal Dimension vs. Relapse of Rectal Cancer

Relapse

imensio

Implementation

• Storage, imaging biomarkers analysis and radiomics.

Local and Cloud solutions.

• 7500 analysis/year and growing

• More than 250 registered users (radiologists,

engineers, biomedical imaging researchers)

Implementation

Oncology

Abdomen

Brain volumetry with parcellationWhite matter lesions detection

State of art

Brain volumetry with parcellation – detailed regions

Longitudinal white matter lesions

Advanced

Diffusion ADC, IVIMPerfusion – Semiquantitative analysis

Texture analysis

Diffusion Kurtosis, StretchedPerfusion – Pharmacokinetics modeling

2D Bone microarchitecture, BMD from CT

3D Bone microarchitecture3D Bone microarchitecture + FE analysis

Cartilage morphologyCartilage T1 mapping

Liver T1 and T2 mapping Liver simultaneous fat & iron quantification

Lung Emphysema Lung low functional densities

Implementation

Study folder

Series folder

info.json

header.json

DICOM files

NIFTI files

biomarker folder

analysis.json

PRECISION CLI APP

Code folder

Code files

*.jade

*.bat call with analysis.json path as input

ANALYSIS OUTPUT

Study folder

biomarker folder

Precision CLI call to retrieve results

Report folder

Results folder

Results retrieval

Images folder

report.xml

Other images

result.xml

report.jade

Series folder

Input/code filesOutput files

analysis.json

Introduce your plugin!

Implementation

AI classification applications

• Chest X-Ray screening

• Deep Learning

• 7470 labeled images

• Normal vs. Abnormal

• AUC=0.91

B-0402 B. Fos Guarinos et al. SS604, ECR 2017

Research agreement

with NVIDIA

Acknowledgements

POST-DOCAlejandro Torreño, PhD - Technology DevelopmentAlejandro Rodríguez, PhD - Image Analysis Engineer

PhD STUDENTSAmadeo Ten - Image Analysis EngineerSara Carratalá - CNS Analysis

CLINICAL TRIALS AND PREBISandra Pérez - Data ManagerJuan Ramón Terrén - Data ManagerRebeca Maldonado - Technician & PREBI

ADMINISTRATIONAna Penadés - Economic & Financial Manager

GIBI230

QUIBIMIMAGE ANALYSIS SCIENTISTSFabio García Castro - Chief Image Analysis ScientistBelén Fos Guarinos - Image Analysis ScientistAna María Jiménez Pastor - Image Analysis ScientistRafael López González - Image Analysis Scientist

DEVELOPMENTRafael Hernández Navarro - Chief Technology OfficerAlejandro Mañas García - Full Stack Senior DeveloperEduardo Camacho Ramos - Front-End Developer

CLINICAL TRIALSIrene Mayorga Ruíz - Clinical Trials CoordinatorRaúl Yébana Huertas - Image Analysis Technician

MARKETING AND COMMUNICATION Katherine Wilisch Ramírez - Marketing Manager

MANAGEMENTIsabel Montero Valle – Team CoordinatorEncarna Sánchez Bernabé - Chief Operating OfficerDaniel Iordanov López - Assistant to Business Development

Luis Martí Bonmatí MD, PhD. GIBI Principal Investigator

QUIBIM Founder

Ángel Alberich Bayarri, PhD. GIBI Scientific-Technical DirectorQUIBIM CEO & Founder

Acknowledgements

Topics for discussion

• The ‘training datasets’ issue: How can we stimulate and

encourage the participation of experts (radiologists,

residents) in segmentation and classification tasks? • Publications, meetings, fee?

• How can we break the data silos?• Experiences in building new biobanks (Valencia – LaFe - Euro-

Bioimaging, Parenchima COST, Italian biobank)

• How can we integrate these solutions in hospitals?• Buy all start-up solutions?

• Through big players? Siemens, Philips, GE, TeraRecon(EnvoyAI)

Transforming images into meaningful

data through AI and imaging biomarkersAngel Alberich-Bayarri, PhD

alberich_ang@gva.es

angel@quibim.com1 Biomedical Imaging Research Group (GIBI230)

La Fe Polytechnics and University Hospital2 QUIBIM SL

Transforming images into meaningful data through AI and...

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