Arvid Lundervold - Visualization · Arvid Lundervold: Computational Medicine and Machine Learning -...

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

The Delft Data Science Seminar: Visual Data Science and its role in Computational Medicine - February 6th 2018

COMPUTATIONAL MEDICINE and MACHINE LEARNING

- Opportunities and Challenges

Arvid Lundervold BSc, MD, PhD

Neuroinformatics and Image Analysis Laboratory Neural Networks Research Group

Department of Biomedicine, University of Bergen, Norway & Mohn Medical Imaging and Visualization Centre, Haukeland University Hospital

(in collaboration with Assoc Prof Alexander S. Lundervold)

www.uib.no|arvidl.github.io|mmiv.no

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

• “Applying methods from engineering, mathematics and computational sciences to improve our understanding and treatment of human diseases”

– Rai Winslow, Director, Institute for Computational Medicine, Johns Hopkins University

• “A new field of science, which embraces mathematics, physics, information technology, biomedical engineering and medicine ... ... to collect, manage, mine, analyse and visualise a very heterogeneous mixture of datalike personalised genetic and proteomic profiles, bio-signals and the monitoring ofmovement, advanced imaging, and other relevant phenotype information in such a way that useful clinical information for improving human health can beextracted.”

– M. Daumer, SLCMSR / Munich&Cambridge International School for Clinical Bioinformatics and Technical Medicine

Similar terms: Systems medicine, computational health informatics

What is “computational medicine” ?

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

The paradigm shift in cell and molecular biology

- from a reductionist approach to an integrative approach

B. O. Palsson, Systems biology: Properties of reconstructed networks, Cambridge University Press, 2006

Quantitative imaging & modelling

/ Systems

medicine

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Medicine and the “new” computational fields

Data Science `producing insights’ e.g. explorative and longitudinal data analysis

Machine Learning `producing predictions’ e.g. biomarkers treatment response

Artificial Intelligence `producing actions’ e.g. imaging-guided robot surgery

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

“Computational organisms ”

fish Zebrafish

flies Drosophila

worms C. elegans

man (in health & disease)

. . . . .

Omics Channels Cells Tissues Organs

http://www.cam.ac.uk/research/features/how-close-are-you-to-a-fruit-fly

http://thezebrafishlab.com/zebrafish-and-human1

https://macaulay.cuny.edu/eportfolios/wormsandlearning/what-is-caenorhabditis-elegans

https://www.nature.com/articles/ncomms8924/figures/2

Its nervous system

contains 302 neurons

Whole-CNS functional imaging of the

isolated Drosophila larval nervous system

using light-sheet microscopy.

http://www.robotspacebrain.com/tag/zebrafish

https://youtu.be/YLVdRPVj-XM?t=9

Watch 80,000 neurons fire

in the brain of a fish

https://www.wired.com/2014/07/neuron-zebrafish-movie/

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Atom 10-12 m

Protein 10-9 m

Cell 10-6 m

Tissue 10-3 m

Organ 100 m Anatomy

Organ system & organism Physiology

Gene networks

Pathway models Stochastic models Ordinary Differential Equations

Continuum models (Partial Differential

Equations)

Systems models

10-6 s molecular events

(ion channel gating)

10-3 s diffusion

cell signalling

100 s motility

103 s mitosis

106 s protein

turnover

109 s human lifetime

Brain

Spinal cord

Peri- pheral nerves

Computational medicine – from molecules to man

Modelling :

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

P4 (predictive, preventive, personalized, and participatory) medicine

- based on systems biology and systems medicine, consumer-driven healthcare and social networks, and the digital revolution ...

Personalized & «P4 medicine»

http://p4mi.org

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

The Mohn Medical Imaging and Visualization Centre

https://mmiv.no/machinelearning

( Bergen )

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

MMIV - Computational medical imaging and machine learning

https://mmiv.no/machinelearning

How we started out ...

Dell Alienware Area-51 2 x NVIDIA GTX 1080 Ti

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Translational molecular imaging

Prof. Frits Thorsen, UiB

Brain metastesis longitudinal MRI

Live cell imaging

www.uib.no/en/rg/mic

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

https://mmiv.no/machinelearning

Visual Exploration and Analysis of Perfusion

Data

Steffen Oeltze, University of Magdeburg

Assessment of kidney volumes and

glomerular filtration rate (GFR)

in CKD, renal cancer, polycystic disease

Kidney structure and function

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

https://mmiv.no/machinelearning

Three challenges: 1 Kidney: find ! (Left and Right)

2 Displacements: correct ! 3 Contrast agent wash-in and wash-out: preserve !

The moving kidney in 3D DCE-MRI

R L

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Fast semi-supervised

segmentation of the

kidneys in DCE-MRI

using convolutional

neural networks (CNN)

and transfer learning

Left & right kidney volumes

Mean SI time courses

~ GFR, ~ RPF

WIP: aorta segmentation (AIF)

~ 50 hr

~ 5 hr

~ 5 sec

without motion-correction

Alexander S. Lundervold et al. Three challenges:

1 Kidney: find ! (Left and Right)

2 Displacements: correct ! 3 Contrast agent wash-in and wash-out: preserve !

https://doi.org/10.1016/j.compmedimag.2008.11.004

Cfr.

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Labelling (2D/3D hand segmentation, delineation)

Performance assessment

Visual inspection

Dice coefficient (w.r.t. “ground truth”)

Ground truth ?

Labeling tools

Crowdsourcing

Inter and intra- rater variation

«Wisdom of the crowd»

«FoldIt»

Ensemble learning

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Neuro-gastroenterology

Brain-gut axis and prediction of IBS from sMRI, dMRI, and resting-state fMRI

Nat. Rev. Gastroenterol. Hepatol.12, 592–605 (2015)

FA map

AD map

RD map

Insula / salience network

Freesurfer 6 segmentation

E. Valestrand

T. Hausken A. Lundervold

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Gynecologic cancer

• Endometrial carcinoma

• Tissue characterization

• Prediction of outcome

(deep myometrial invasion

lymph node metastases, survival)

Texture analysis applied to motion-corrected multiparametric MRI in the assessment of endometrial carcinoma

DCE-MRI

Texture features

Sigmund Ytre-Hauge

Precision Imaging in Gynecologic Cancer

in collaboration with

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Prostate cancer

• Multi-parametric MRI and histopathology

• From digital pathology to

computational pathology

Are Losnegård et al.

Computerized Medical Imaging and Graphics 63 (2018) 24–30

Histology MRI

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Open science – Reproducible research - GitHub

https://arvidl.github.io

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Infrastructure

Titan V

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Teaching and training BSc / MSc / MD / PhD

EU ERASMUS+ Strategic partnership 2017/Q4 – 2020/Q3 365.000 EUR

Open Educational Resources in Computational Biomedicine

Coordinated from Bergen

Bergen, Karolinska Institutet, Turku, Kuopio, Odense

ELMED219: Joint UiB & HVL course BMED360 DAT157

e-learning

Open edX

https://akademix.no https://nordbiomed.akademix.no

http://www.uib.no/en/course/ELMED219

https://github.com/AkademiXno/Akademix-workshop-OERCompBiomed

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Thanks !

http://www.uib.no/en/persons/Arvid.Lundervold http://alexander.lundervold.com

https://mmiv.no

Arvid Lundervold: Computational Medicine and Machine Learning - Opportunities and Challenges

Francisco Azuaje

Luxembourg Institute of Health

Vincent Barra

ISIMA, Clermont-Ferrand Bradley J. Erickson

Mayo Clinic, Rochester

Hans-Christian Hege

Zuse-Institute Berlin

Marek Kocinski

TUL, Lodz

Andrzej Materka

TUL, Lodz

Robert Jenssen

Machine Learning @ UiT Lab

Ender Konukoglu

ETH Zurich

https://mmiv.no/machinelearning

MMIV – Computational Medical Imaging and Machine Learning: External collaborators