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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