mitochondria detection

Mitochondria Detection in 3D Brain Images

Joy Patel and Courtney Smith

July 22, 2014

Joy Patel and Courtney Smith Mitochondria Detection in 3D Brain Images

Introduction

How can we identify mitochondria in 3Dimage sets of a brain?


Introduction

Data Sets We are Working With

1Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks withLearned Shape Features


Introduction

Data Sets We are Working With


Introduction

Outline

(i) Why mitochondria?(ii) Methods we used and their results(iii) Conclusion and moving forward


Introduction

What is the Open Connectome project?

1Volumetric Exploitation of Synaptic Information using Context Localizationand Evaluation


Mitochondria

How does mitochondria detection play arole in the Open Connectome project?

1Supervoxel-Based Segmentation of Mitochondria in EM Image Stacks withLearned Shape Features


Mitochondria

Characteristics of Mitochondria


Mitochondria Detection

GoalsCreate a program that detects mitochondria in brain images

1 Properly segment the images2 Identify features to train and to classify mitochondria3 Improve boundaries of detected mitochondria


Ground Truth

Ground Truth

Figure : Highlighted Mitochondria


Earlier Stages

Earlier Methods


Earlier Stages

Hough Transform


Earlier Stages

Edge Histogram

Figure : Left: Slide Used, Right: Masked Edges


Earlier Stages

Edge Histogram

Figure : Histograms of Object Edges


Earlier Stages

Mitochondria Histograms

Figure : GUI with Image Histograms


Earlier Stages

Gabor Filters

Figure : Gabor Atoms


Earlier Stages

Gabor Filters


Image Find

SURF Detection

Figure : Left: SURF Feature Points in Object, Right: SURF FeaturePoints in Mitochondria


Earlier Stages

SURF Detection

Figure : Detected Matching Points


Earlier Stages

Normalized Cross Correlation



Best Working Pipeline of MitochondriaDetection



Best Working Pipeline of MitochondriaDetection

1 Use Bilateral Filter on Image2 Supervoxelize the Image3 Extract Feature Vector

(a) Shell Statistics/Histogram(b) Bag of Words

4 Random Forest Detection5 Active Contours


Bilateral Filter

Bilateral Filter


Supervoxels

Supervoxelizing the whole image

Figure : Left: Full Slide, Right: Zoomed In


Supervoxels

Supervoxels

1SLIC Superpixels Compared to State-of-the-art Superpixel MethodsJoy Patel and Courtney Smith Mitochondria Detection in 3D Brain Images

Machine Learning

Shell Statistics/Histogram Vectors

Shell Statistics: Mean, Median, Variance, 25th Percentile, 75th PercentileGrayscale Intensities and Mean Image Gradient Magnitude for wholeSuperVoxel and radius: [0,3],(3,6],(6,9],(9+).Histogram Grayscale Vector


Machine Learning

Bag of WordsMaking CodeBook:

Segment image into 10-by-10 patches.Convert patches into a R100 vectors.Concatenate all R100 vectors into a k-by-100 matrix.Do k-means with k=100; output from k-means is CodeBook.


Machine Learning

Bag of Words

Figure : Codebook


Machine Learning

Bag of Words


Machine Learning

Use information gain to decide splits

Ij = H(Sj)−∑

i∈{L,R}

|S ij ||Sj |

H(S ij )

1www.cs.ubc.ca/ nando/540-2013/lectures.l9.pdfJoy Patel and Courtney Smith Mitochondria Detection in 3D Brain Images

Machine Learning

Random Forest


Machine Learning

Random Forest

Figure : Building a Forest (ensemble)


Supervoxels

Machine Learning



Active Contour

1Image Segmentation Using the Chan-Vese Algorithm - Robert CrandallJoy Patel and Courtney Smith Mitochondria Detection in 3D Brain Images

Active Contour

Selected Centerpoints vs. Supervoxels


Results

Results


Ground Truth

3D Visualization of ResultsWith Connected Components


Results

Percent of 3D Connected Components Truth Mitochondria:

50 to 100 percent Overlap Detected by Supervoxels:74.157320 to 50 percent Overlap Detected by Supervoxels:15.73030 to 20 percent Overlap Detected by Supervoxels:2.24720 percent Overlap Detected by Supervoxels:7.8652

0Detection: about 3100 secondsJoy Patel and Courtney Smith Mitochondria Detection in 3D Brain Images

Supervoxels

Machine Learning

Percent of 2d Supervoxels Detected:True Positives: 86.6092False Positives: 13.3908


Conclusion

(i) Why mitochondria plays a vital role in the Open Connectomeproject

(ii) Methods we have used and their results

We have seen that supervoxels and random forest used with activecontours have given us the best results for mitochondria detection.Through our research we have noticed that others have beenpublishing papers where they are implementing similar methodsthat we have used.


ConclusionFurther Research

Implement Supervoxel process in 3D and compare results.Determine a better feature space for machine learning.


A special thanks to:

1 Duke Math RTG2 Dr. Guillermo Sapiro3 Dr. Paul Bendich4 Dr. Joshua Vogelstein5 Dr. John Harer6 Dr. Rann Bar-On7 Dr. Robert Calderbank8 Chris Tralie9 William R. Gray10 Dean Kleissas11 Kathy Peterson


mitochondria detection

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