The Brain From Histological Images Prof. Dr. Katrin Amunts Dr. - - PowerPoint PPT Presentation

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)