IDEA
Patch-driven Neonatal Brain MRI Segmentation
with Sparse Representation and Level Sets
Li Wang1, Feng Shi1, Gang Li1, Weili Lin1, John H. Gilmore2, Dinggang Shen1
1
Department of Radiology and BRIC,
2 Department of Psychiatry,
University of North Carolina at Chapel Hill, NC, USA
Department of Radiology and BRIC, UNC-Chapel Hill
Content

Introduction
 Proposed method
 Experimental results
 Discussion and conclusion
Department of Radiology and BRIC, UNC-Chapel Hill
Introduction

Accurate segmentation of neonatal brain MR images
into WM, GM and CSF is essential in the study of
infant brain development.
 lower tissue contrast, severe partial volume effect, high
image noise, and dynamic white matter myelination.
Neonatal image
Adult image
Department of Radiology and BRIC, UNC-Chapel Hill
Introduction
Atlas-based Methods
•Population-based atlas
complex brain structures are generally diminished due to inter-subject anatomical variability
Original
WM
GM
CSF
•Can we build a subject-specific atlas?
Department of Radiology and BRIC, UNC-Chapel Hill
Proposed method
Step 1
…
Testing subject
Template images
Subject-specific atlas
Step 2
Local spatial consistency
Step 3
Level set
segmentation
Final segmentation
Department of Radiology and BRIC, UNC-Chapel Hill
Step1: Constructing a subject-specific atlas
from population
Template images
Testing subject
X:
D:[
WM
CSF ]
GM
2
1
2
min X  D 2  1  1 

 0 2
2
2
2
α=
=
Department of Radiology and BRIC, UNC-Chapel Hill
Comparison of subject-specific and populationbased atlas
Original T2
image
Populationbased atlas
Subject-specific
atlas
Department of Radiology and BRIC, UNC-Chapel Hill
Step2: local spatial consistency in the testing image
space
2
1
2
2




min X  D  2  1  1 
 2
  0 2
2
Step 1: subject-specific atlas
Department of Radiology and BRIC, UNC-Chapel Hill
Step 3: level set segmentation
Department of Radiology and BRIC, UNC-Chapel Hill
Experimental results

Parameters selection
Sum Dice ratios of WM and GM
1.83
1.81
1.79
1.77
1.75
1.73
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36
λ1 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.01 0.1 0.2 0.5 0.5 0.2 0.1 0.01
w
3
3
3
3
3
3
3
3
3
3
3
3
5
5
5
5
5
5
5
5
5
5
5
5
7
7
7
7
7
7
7
7
7
7
7
7
wp
3
3
3
3
5
5
5
5
7
7
7
7
3
3
3
3
5
5
5
5
7
7
7
7
3
3
3
3
5
5
5
5
7
7
7
7
WM+GM 1.78 1.79 1.79 1.78 1.77 1.79 1.79 1.78 1.75 1.78 1.78 1.77 1.81 1.82 1.82 1.81 1.81 1.82 1.82 1.81 1.81 1.82 1.83 1.81 1.8 1.8 1.8 1.78 1.81 1.81 1.81 1.79 1.8 1.82 1.82 1.82
The weight for L1-term λ1=0.1, weight for L2-term λ2=0.01, patch size 5×5×5, local
searching window 5×5×5.
Department of Radiology and BRIC, UNC-Chapel Hill
Template numbers?
How many template images are needed to generate a good segmentation?
Box-whisker plots of Dice ratio of segmentation using an increasing number of
templates from the library. Experiment is performed by leave-one-out using the
library of 20 templates.
Department of Radiology and BRIC, UNC-Chapel Hill
Leave-one-out cross validation on 20 subjects
WM
0.93
0.91
0.89
0.87
0.85
Dice ratio
0.83
MV
CLS
CPM
Subject-specific-atlas
Proposed (without spatial consistency)
Proposed (with spatial consistency)
0.81
0.79
0.77
0.75
0.73
0.71
0.69
0.67
0.65
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Subject
M V: Majority voting
CLS (Coupled level sets): Wang, L., et al., 2011. Automatic segmentation of neonatal images
using convex optimization and coupled level sets. NeuroImage 58, 805-817.
CPM (Conventional patch-based method): Coupe, P.,et al., 2011. Patch-based segmentation
using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54,
940-954.
Department of Radiology and BRIC, UNC-Chapel Hill
Leave-one-out cross validation on 20 subjects
GM
0.93
0.91
0.89
0.87
0.85
Dice ratio
0.83
MV
CLS
CPM
Subject-specific-atlas
Proposed (without spatial consistency)
Proposed (with spatial consistency)
0.81
0.79
0.77
0.75
0.73
0.71
0.69
0.67
0.65
1
2
3
4
5
6
7
8
9 10 11 12 13 14 15 16 17 18 19 20
Subject
M V: Majority voting
CLS (Coupled level sets): Wang, L., et al., 2011. Automatic segmentation of neonatal images
using convex optimization and coupled level sets. NeuroImage 58, 805-817.
CPM (Conventional patch-based method): Coupe, P.,et al., 2011. Patch-based segmentation
using expert priors: Application to hippocampus and ventricle segmentation. NeuroImage 54,
940-954.
Department of Radiology and BRIC, UNC-Chapel Hill
8 testing subjects with manual segmentations
0.94
0.92
(a) Original
(b) CLS
(c) CPM
Dice ratio
0.9
(d) Proposed
0.88
CLS
0.86
CPM
Proposed
0.84
0.82
0.8
WM
GM
CLS: Coupled level set
CPM: Conventional patch-based method
(e) CLS
(f) CPM
(g) Proposed
WM difference
(h) CLS
(i) CPM
(j) Proposed
GM difference
Department of Radiology and BRIC, UNC-Chapel Hill
94 testing subjects for qualitative evaluation
Original
CPM
CLS
Proposed
Original
CLS
CPM
Proposed
CLS: Coupled level set
CPM: Conventional patch-based method
Department of Radiology and BRIC, UNC-Chapel Hill
Images with different scanning parameters
sequence #2
sequence #3
sequence #4
Department of Radiology and BRIC, UNC-Chapel Hill
Conclusion



In this paper, we proposed a novel patch-driven level sets
method for neonatal brain MR image segmentation.
The average total computational time is around 120 mins for the
segmentation of a 256×256×198 image with a spatial resolution
of 1×1×1 mm3 on our linux server with 8 CPUs and 16G
memory.
Our future work will include more representative subjects
(normal/abnormal) as templates.
Department of Radiology and BRIC, UNC-Chapel Hill

Source code can be found:
http://www.unc.edu/~liwa
 Google: li wang unc
Department of Radiology and BRIC, UNC-Chapel Hill