CHIPS - A Service for Collecting, Organizing, Processing, and Sharing Medical Image Data in the Cloud

 

Rudolph Pienaar, PhD

 

 

Technical Director
Fetal-Neonatal Neuroimaging and Developmental Science Center


Staff Scientist
Boston Children's Hospital
 


introduction

Healthcare informatics is at an inflection point

This talk considers a particular perspective on the problem and proposes a solution

Cloud

Healthcare

Information

Processing

Service

CHIPS


overview

Some trends confronting computing in healthcare and shaping the landscape

An introduction to our web-based system called CHIPS

Logical topology / design

UI considerations

Containerization

Healthcare informatics landscape

Clouds and untethered computing

Historical legacy


overview

you have to know the past 

to understand the present

Carl Sagan




















overview


overview


Information explosion...

Annual global IP traffic will pass the zettabyte (1000 exabytes) threshold by the end of 2016, and will reach 2 zettabytes per year by 2019. 

  • By 2016, global IP traffic will reach 1.1 zettabytes per year, or 88.4 exabytes (nearly one billion gigabytes) per month, and by 2019, global IP traffic will reach 2.0 zettabytes per year, or 168 exabytes per month.

Global IP traffic has increased fivefold over the past five years, and will increase threefold over the next five years.

  • Overall, IP traffic will grow at a compound annual growth rate (CAGR) of 23 percent from 2014 to 2019.
http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/VNI_Hyperconnectivity_WP.html
        

Information explosion...

Two-thirds of all IP traffic will originate with non-PC devices by 2019.

  • In 2014, only 40 percent of total IP traffic originated with non-PC devices, but by 2019 the non-PC share of total IP traffic will grow to 67 percent. 

Traffic from wireless and mobile devices will exceed traffic from wired devices by 2016.

  • By 2016, wired devices will account for 47 percent of IP traffic, and Wi-Fi and mobile devices will account for 53 percent of IP traffic. In 2014, wired devices accounted for the majority of IP traffic, at 54 percent.
http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/VNI_Hyperconnectivity_WP.html
        

Information explosion...

Global Internet traffic in 2019 will be equivalent to 66 times the volume of the entire global Internet in 2005.

  • Globally, Internet traffic will reach 37 gigabytes (GB) per capita by 2019, up from 15.5 GB per capita in 2014.

The number of devices connected to IP networks will be more than three times the global population by 2019

  • There will be more than three networked devices per capita by 2019, up from nearly two networked devices per capita in 2014. Accelerated in part by the increase in devices and the capabilities of those devices, IP traffic per capita will reach 22 GB per capita by 2019, up from 8 GB per capita in 2014.
http://www.cisco.com/c/en/us/solutions/collateral/service-provider/visual-networking-index-vni/VNI_Hyperconnectivity_WP.html
        

Information explosion...


Information explosion...

Healthcare vs Financial

  • Financial
    • Number of accounts: 10,000 to 300 million
    • Storage per Account: GB
    • Total Storage: 10s of TB to 300 PB
  • Healthcare
    • Number of patients: 10,000 to 300 million
    • Storage per Patient: GB today, TB tomorrow
    • Total Storage: 20 PB to 600 EB

Information explosion...

Healthcare


Information explosion...

Healthcare

  • 1.65M tumors measured in various modalities
  • > 4 exabytes of data in various localities
  • 1 petabyte of data (0.001EB) to transfer 
  • On current Internet, would take about 21 days to transfer this data around.

an operating theatre...


healthcare... philosophy shapes informatics

historically, healthcare is differential, not integrative

informatics is poorly understood and not seen necessarily as fundamental

classically information relationship is best described as 1:1

the practice of medicine is less integrative


healthcare "compute"...

provided by vendors with very specialized skills and market

thin, vertical stacks


Typical hospital "web" apps...


Typical hospital "web" apps...


Typical hospital "web" apps...


healthcare informatics landscape...

Healthcare informatics is at an inflection point

Need: a platform that combines multiple data and multiple compute on useful hardware

explosion of sensors

complex analytics

multiple possible data sources

lack of local compute resources


Forces acting on healtcare workflows

Compounding Complexity

  • Data is distributed in multiple places
    • EMRs
    • Modality Databases
    • Department databases
    • Different formats
  • Data is structured and unstructured
    • "Voice" recordings
    • "Free form text"

Forces acting on healtcare workflows

Compounding Complexity

  • Inconsistent/variable definitions
    • Quantitative interpretations
    • Qualitative measurement variability
      • T(R) on Siemens might be different to GE
  • Data itself is complex
    • genomics
    • radiomics (images)
    • combinatorial explosion

Typical hospital "web" apps...

Current App Philosophy

  • Old-style "web" apps subsumed to look like desktop apps.
    • Reflect an "app" centric model, not "data" centric.
  • Not really "web" apps:
    • Typically limited to obsolete versions of Internet Explorer
    • No support for mobile or other platforms.
  • No cross-app integration on data level.

some trends in informatics...

disparate, disconnected computing

the primacy of the browser as client platform

the rise of the ...


silos...

Multiple, deep data sets exist in the healthcare dataverse

Largely disconnected and independent of each other

clinical

billing

health records


sparse dataverse...

data points, rich as they are, can be insufficient...

the more connections between points...


data needs "connectedness"

the better!


patterns of information flow in healthcare

Current

  • Unidirectional
  • Isolated
  • Non-integrative
  • Static

Future

  • Multi-directional
  • Collaborative
  • Integrative
  • Dynamic

clouds and clouds and clouds...

what are "clouds"?

remember above all others that a "cloud" is really just...

someone else's computer out on the network


what do clouds do?

well... they rain...

essentially, we often think of "cloud" computing as less of "computing" and more as storage

we use "clouds" as hard drives in the sky

pull data (i.e. rain) and consume it locally


medical compute... in the cloud

definite need for data mobility and centralization...

with compute being "free" to process data where-ever it might naturally collect...


CHIPS...

CHIPS is a bio-medical data workflow manage that allows easy and intuitive collection, analysis, and sharing of data between parties.

  • allows for advanced view rendering
  • allows for sharing of images
  • allows for immersive real time collaboration
  • allows for post-processing of images

many data sources... many compute sources...

via here

connect data here

to here...

and compute!


system topology

Main web server local data repository

remote compute nodes

data source (e.g. in hospital PACS)


distributed design...

web server

coordinator

data handler

compute handler


distributed design...

Red Hat collaboration


containerization


containerization


compute and io...

disparate, disconnected computing

data needs to be transferred from server host to remote location

data needs to be processed on remote location


io

local web server

data descriptor

data handler


io

pfurl --verb POST --raw --http 172.17.0.2:5055/api/v1/cmd --msg \
'{  "action": "pushPath",
    "meta": {
        "remote": {
            "key":         "someKey"
        },
        "local": {
            "path":         "/home/data"
        },
        "transport": {
            "mechanism":    "compress",
            "compress": {
                "encoding": "base64",
                "archive":  "zip",
                "unpack":   true,
                "cleanup":  true
            }
        }
    }
}' --quiet --jsonpprintindent 4  

compute

local web server

exec descriptor

exec handler


compute

pfurl --verb POST --raw --http 172.17.0.2:5010/api/v1/cmd \
     --jsonwrapper 'payload' --msg \
'{  "action": "run",
        "meta": {
                "cmd":      "cal 7 1970",
                "auid":     "rudolphpienaar",
                "jid":      "cal-job-1234",
                "threaded": true
        }
}' --quiet --jsonpprintindent 4

compute... using a container

pfurl --verb POST --raw --http 10.17.24.163:5010/api/v1/cmd 
--jsonwrapper 'payload' --msg '
        {   "action": "run",
            "meta":  {
                "cmd":      "$execshell $selfpath/$selfexec --prefix test- --sleepLength 0 /share/incoming /share/outgoing",
                "auid":     "rudolphpienaar",
                "jid":      "simpledsapp-1",
                "threaded": true,
                "container":   {
                        "target": {
                            "image":            "fnndsc/pl-simpledsapp",
                            "cmdParse":         true
                        },
                        "manager": {
                            "image":            "fnndsc/swarm",
                            "app":              "swarm.py",
                            "env":  {
                                "shareDir":     "/home/tmp/share",
                                "serviceType":  "docker",
                                "serviceName":  "testService"
                            }
                        }
                }
            }
        }
'

coordination

timing can be complex...

PUSH data

separate coordinator service

WAIT!

EXEC on data

WAIT!

PULL data

WAIT!


coordination


security

  • Medical data is constrained by many external factors


     
  • Logging and control
  • Single shutoff point

     
  • Communication pathways
  • HIPAA
  • Local regulatory
  • IRB

secure architecturally


secure communication


big data

  • Imaging


     
  • Genomics
  • Pathology
  • Input DICOM space
  • Processed result space
  • Structured clinical reporting

big data...


UI concepts


UI concepts


UI concepts


conclusion

  • Healthcare compute is at an inflection point
  • Lags larger computing -verse
  • Many opportunities and challenges exist

Untapped opportunity to integrate cutting edge approaches to inform healthcare


references... CHIPS/ChRIS

  • https://github.com/FNNDSC/ChRIS_ultron_backEnd
  • https://github.com/FNNDSC/ChRIS_ultron_frontEnd
    

references... services

 
  • https://github.com/FNNDSC/viewerjs
  • https://github.com/FNNDSC/gcjs
  • https://github.com/FNNDSC/fmjs
  • https://github.com/FNNDSC/toolbarjs
  • https://github.com/FNNDSC/rboxjs
  • https://github.com/FNNDSC/rendererjs
  • https://github.com/FNNDSC/thbarjs
  • https://github.com/FNNDSC/pman
  • https://github.com/FNNDSC/pfioh
  • https://github.com/FNNDSC/pfcon

references... viewers

  • https://goxtk.com
    
  • https://github.com/FNNDSC/ami

team

  • Jorge Bernal
  • Nicolas Rannou
  • Rudolph  Pienaar
  • Yangming Ou
  • Daniel Haehn
  • Daniel Ginsburg
  • Ellen Grant

BCH Team

BU Team

  • Orran Krieger
  • Ata Turk
  • Aditya Awalker

RedHat

  • Dan McPherson
  • and others!

fin!

Thank you!

FTC_2017

By Rudolph Pienaar

FTC_2017

Future Technology Conference 2017

  • 1,103