INSTITUTE OF NEUROSCIENCE AND MEDICINE (INM-1)

Using Multiple GPUs To Reconstruct

The Brain From Histological Images

Prof. Dr. Katrin Amunts

Dr. Markus Axer

Dr. Timo Dickscheid

Jiri Kraus

High Resolution

Image Data

Requirements:

1 2 Accurate Reconstruction

Algorithms

Part 1

Data & Algorithm

Preparation

Imaging

Preparation

Imaging

Analysis*

* M. Axer, A novel approach to the human connectome (NeuroImage, 2011)

Preparation

Imaging

Analysis*

Registration

Blockface images (800 sections)

Histologies (150 sections)

* M. Axer, A novel approach to the human connectome (NeuroImage, 2011)

* D. Rueckert, Nonrigid registration using free-form deformations (IEEE Trans Med Imaging, 1999)

1. Geometric Transformation (B-Spline model)*

2. Metric function (Mutual Information, MI)**

3. Optimizer

* D. Rueckert, Nonrigid registration using free-form deformations (IEEE Trans Med Imaging, 1999)

** J. Pluim, Mutual information based registration of medical images (IEEE Trans Med Imaging, 2003)

1. Geometric Transformation (B-Spline model)*

Histologies (b-spline):

Blockfaces:

Histologies (affine):

Blockfaces:

• Layers: 1000

• Grid size: 10x10

Assumptions:

200.000 Parameters

Solution:

• Efficient global metric

• Efficient optimizer (MRF)

Efficient global metric*:

* B. Glocker, Dense Image Registration through MRFs and efficient linear programming (Medical Image Analysis, 2008)

M. Feuerstein, Reconstruction of 3D Histology Images by Simultaneous Deformable Registration (MICCAI, 2011)

1. Similarity between Histology and Blockface

2. Similarity between consecutive Histologies

Efficient global metric*:

1. Similarity between Histology and Blockface

(#Displ · #Nodes · #Images) Data Terms

40 · 100 · 1000 = 4.000.000

2. Similarity between consecutive Histologies

* B. Glocker, Dense Image Registration through MRFs and efficient linear programming (Medical Image Analysis, 2008)

M. Feuerstein, Reconstruction of 3D Histology Images by Simultaneous Deformable Registration (MICCAI, 2011)

1. Similarity between Histology and Blockface

(#Displ · #Nodes · #Images) Data Terms

40 · 100 · 1000 = 4.000.000

2. Similarity between consecutive Histologies

(#Displ2 · #Nodes · #Gaps) Data Terms

402 · 100 · 999 ≈ 160.000.000

3. Optimizer (MRF*): Best node displacements

Efficient global metric*:

* B. Glocker, Dense Image Registration through MRFs and efficient linear programming (Medical Image Analysis, 2008)

M. Feuerstein, Reconstruction of 3D Histology Images by Simultaneous Deformable Registration (MICCAI, 2011)

1. Similarity between Histology and Blockface

(#Displ · #Nodes · #Images) Data Terms

40 · 100 · 1000 = 4.000.000

2. Similarity between consecutive Histologies

(#Displ2 · #Nodes · #Gaps) Data Terms

402 · 100 · 999 ≈ 160.000.000

3. Optimizer (MRF*): Best node displacements

Refinement

(few 100 iterations)

Efficient global metric*:

* B. Glocker, Dense Image Registration through MRFs and efficient linear programming (Medical Image Analysis, 2008)

M. Feuerstein, Reconstruction of 3D Histology Images by Simultaneous Deformable Registration (MICCAI, 2011)

Simultaneous (global) Registration:

Section-wise Registration:

(150 CPUs 14 hours)

Part 2

GPU-accelerated Implementation

1. Distribution of the image sections among mutliple GPUs

2. Each GPU delivers the data terms depending on its

assigned image sections

calcDataTerms_Horizontally( bf_image, histo_image, nodeDisp d)

• establishJointHistograms(d) 100 Joint Histograms

• establishMarginalHistograms() 300 Histograms

• calculate_MIValues() 100 MI values

calcDataTerms_Vertically( histo_1, histo_2, d1, d2 )

• establishJointHistograms(d1,d2) 100 Joint Histograms

• establishMarginalHistograms() 300 Histograms

• calculate_MIValues() 100 MI values

JuDGE (Westmere + Fermi) PSG-Cluster (Ivy Bridge + Kepler)

2 images with 1 GPU (PSG-Cluster)

M2090 K40

81 %

30 %

1. Multiple CUDA-streams (Double Buffering)

40 sec 33 sec (17.5 % on Kepler)

33 sec

342 sec

2. Incremental atomic operations

Non-atomic: 92 sec vs. 39 sec (2.4 x)

Atomic: 342 sec vs. 33 sec (10.3 x)

Transfer additional load to the GPU!

• Multiple GPUs offer the power to solve a simultaneous registration within a

reasonable time

• In the future: Optimization for microscopic images (memory limitations)

INM, Research Centre Jülich

Prof. Dr. Katrin Amunts

Dr. Markus Axer

Dr. Timo Dickscheid

David Graessel

Philipp Schlömer

Daniel Schmitz

Martin Schober

Nicole Schubert

JSC, Research Centre Jülich

Oliver Bücker

Andrew V. Adinetz

Nvidia Support

Jiri Kraus

Contact: Marcel Huysegoms, m.huysegoms@fz-juelich.de

Appendix

Preparation

Imaging

Low Resolution:

Size: 3.000 × 3.000 pixel

Pixel size: 64 μm × 64 μm

File size: 10 MB (8 bit)

High Resolution:

Size: 100.000 × 100.000 pixel

Pixel size: 1.3 μm × 1.3 μm

File size: 10 GB (8 bit)

Analysis*

* M. Axer, A novel approach to the human connectome (NeuroImage, 2011)

1. Distribution of the image sections among mutliple GPUs

2. Each GPU delivers the data terms depending on its

assigned image sections

calcDataTerms_Horizontally( bf_image, histo_image, nodeDisp d)

• establishJointHistograms(d) 100 Joint Histograms

• establishMarginalHistograms() 300 Histograms

• calculate_MIValues() 100 MI values

1000

X

40

times

calcDataTerms_Vertically( histo_1, histo_2, d1, d2 )

• establishJointHistograms(d1,d2) 100 Joint Histograms

• establishMarginalHistograms() 300 Histograms

• calculate_MIValues() 100 MI values

999

X

1600

times

JuDGE (Westmere + Fermi) vs. PSG-Cluster (Ivy Bridge + Kepler)