Medical Image Processing with OpenShift and OpenStack

Transcription

1 Medical Image Processing with OpenShift and OpenStack Rudolph Pienaar Ata Turk Staff Scientist Boston Children s Hospital Research Scientist Boston University Dan McPherson Sr. Principal Software Engineer Red Hat

2 The Landscape Medical image processing research happens in relative isolation The focus is on the output Everyone invents their own platform Brorfelde landscape by Stig Nygaard, Creative Commons Attribution 2.0

3 ChRIS ChRIS (ChRIS Research Integration System) is a web-based medical image platform that allows for various forms of medical image (Ex: MRIs) processing. ChRIS itself is comprised of multiple open source projects ( and the intention is to make the research and capabilities available to other hospitals.

4 Massachusetts Open Cloud (MOC) Collaborative effort bw/ 5 research universities, government, and industry Unlike Amazon/Microsoft/Google an open ecosystem of cloud services Research and innovation over a production-scale public cloud - Operating over a 15MW datacenter - Backed by OpenStack and OpenShift

5 Combined Goal Improve the scale and efficiency of healthcare image processing applications using various Red Hat technologies Today image processing takes ~10 hrs for certain apps. Ideally we can get that down to a few mins. Democratize medical image application development Standardize healthcare application development (docker/oci) Provide a platform that enables testing over real data (OpenShift+OpenStack)

6 The Technology

7 OCI / docker Standardizes image processor development Portability Dependency isolation

8 OpenShift / Kubernetes Provides scaled job framework for running image processors Resource Management CPU/Memory/Network/GPU Easy to operationalize (logging/metrics/ansible/etc.)

9 OpenShift : Optimizing for Density Utilize slack resources with QOS Tiers RESERVED FREE CONSUMED SLACK CONSUMED OR MASSACHUSETTS OPEN CLOUD (MOC)

10 OpenShift - AI/ML Spark Tensorflow...

11 OpenStack OpenShift/Kubernetes doesn t solve the infrastructure problem Containers need a unifying story for the private cloud On-premises IaaS Compute (GPU and CPU) Networking Storage (Ceph) Scale & on-demand access

12 Medical Processing Registration morph one image onto another Used for: localization difference typical first step

13 Medical Processing Classification (AI/ML) Identification and pointing out abnormalities Complex contrasts Metabolism Monte Carlo simulation of photon propagation through brain tissue Multiple wavelengths, scattered based on tissue properties and local metabolism of O2

14 Medical Processing Tractography acquire multiple image volumes solve diffusion equation for water movement based on image contrast mapping fiber paths through the brain for surgical planning

15 ChRIS Detail Store Compute Environment ChRIS Data Source Data Source Data Source Compute Environment Compute Environment

16 Pa ChRIS Detail Store Pb P I1 n Pa O1 Compute Environment I1 I2 I3 I2 I1 ChRIS I3 Data Source O3 O2 Pb I2 O2 Compute Environment O1 I3 Pc O3 Compute Environment

17 Architecture BCH ChRIS Operation Data MOC (OpenStack) OpenShift IO Handler Process Manager Job Swift Init Container Image Plugin 1 Publish Container Image Plugin 2... Image Plugin N Shared Volume

18 Parallelizing Plugins

19 Image Processing Parallel Processing with Kubernetes Job Framework N workers run until worker 1-N completes (fixed completion count) Communication through FS or Network Container 1 (Node A) Container 2 (Node B) Container 3 (Node C) Container 4 (Node D)

20 Parallel Example - Advanced Normalization Tools Among the highest quality algorithms for non-rigid registration Used as prototype for scaling image plugins Lessons Learned Setup Most existing image processing applications weren t written to scale horizontally Performance gains dependent on how much of the algorithm is parallelizable Parallelizable Processing Coalesce

21 GPU Enabled Plugins

22 GPU Topology MOC (OpenStack) HOST X VM 1 VM 2 CONTAINER 1 CONTAINER 1 CONTAINER 2 CONTAINER 2 CONTAINER N CONTAINER N

23 GPU Details Plugins register themselves with ChRIS as requiring or desiring a GPU OpenShift/Kubernetes schedules plugins onto nodes with available GPUs Container 1 (Node A) Container 2 (Node B) Container 3 (Node C) Container 4 (Node D) Device Plugins Gpu icon by Misha Petrishchev from the Noun Project, CC-BY 3.0

24 GPU Example - Prostate Segmentation

25 GPU Example - Monte Carlo

26 Demo

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28 Take Aways Choosing the right open source solutions is essential to every project s success OpenShift on OpenStack solved our processing and data management challenges and let us focus on the specific problem at hand

29 THANK YOU plus.google.com/+redhat facebook.com/redhatinc linkedin.com/company/red-hat twitter.com/redhat youtube.com/user/redhatvideos